Business Problem: Excess steel inventory is hurting profits.
Business Objectives: Minimize excess steel inventory to improve profitability.
Business Constraints: Maximize production efficiency.
Success Criteria:
Business Success Criteria: Reduce steel inventory by 10% to 12% over the next 5 years.
ML Success Criteria: Achieve less than 15% error value in the forecasted inventory levels over the next 5 years.
Economic Success Criteria: Increase profits by $1 million to $1.2 million over the next 5 years, as compared to the previous 5 years average profits.
import pandas as pd # Data Manipulation
import numpy as np # Arthimetic Operations
import matplotlib.pyplot as plt # Data Visualizations
import seaborn as sns # Advanced Data Visualizations
import scipy.stats as stats # Scientific Calculations
import psycopg2
from sklearn.impute import SimpleImputer # Imputation
from sklearn.pipeline import Pipeline # Creating Pipeline
from sklearn.compose import ColumnTransformer
import statsmodels.api as sm
from pylab import rcParams
from statsmodels.tsa.stattools import adfuller
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from pandas.plotting import autocorrelation_plot
# Train and Test split
from sklearn.model_selection import train_test_split
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.tsa.statespace.sarimax import SARIMAX
from statsmodels.tsa.holtwinters import ExponentialSmoothing
# Metrics calculations
from math import sqrt
from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error
from tabulate import tabulate
# Auto-EDA
import sweetviz as sv
# Saving the pipeline
import joblib
# Saving the pickle file
import pickle
# Ignoring Warning
import warnings
warnings.filterwarnings('ignore')
# Connect to the PostgreSQL database
conn = psycopg2.connect(
host ="localhost",
database = "db",
user = "postgres",
password = "Ankita@1603"
)
with conn.cursor() as cur:
cur.execute("SELECT * FROM steel_rods")
rows = cur.fetchall()
for row in rows:
print(row)
data = pd.DataFrame(rows, columns = ["Date", "Time", "Location", "Sales_in_Rs_per_T", "Sales_volume_in_Tonnes",
"Price_per_kg", "Climate", "Customer_ID", "Diameter", "Length",
"Grade", "Current_stock", "Re_order", "Lead_time", "Production_time",
"Units_Produced", "Production_cost"])
(datetime.date(1995, 10, 14), datetime.time(11, 8), 'Pune', 424440, Decimal('7.07'), 60, 'Rainy', 'C9D3791C', 32, 13, 'Fe-500', 10770, 618, 7, 3, 14828, 18734)
(datetime.date(1995, 10, 15), datetime.time(10, 7), 'Hyderabad', 426000, Decimal('8.35'), 51, 'Rainy', 'F515841D', 12, 7, 'Fe-415', 8039, 1195, 2, 3, 12795, 21294)
(datetime.date(1995, 10, 16), datetime.time(17, 25), 'Kolkata', 429360, Decimal('6.82'), 63, 'Rainy', 'B387F82E', 12, 9, 'Fe-500D', 7328, 1372, 14, 6, 13576, 19759)
(datetime.date(1995, 10, 17), datetime.time(10, 0), 'Bengaluru', 432000, Decimal('6.86'), 63, 'Rainy', 'A8EBB29A', 8, 25, 'Fe-550', 3040, 688, 14, 2, 8060, 23073)
(datetime.date(1995, 10, 18), datetime.time(16, 39), 'Delhi', 432000, Decimal('7.45'), 58, 'Rainy', 'DC1D8CD9', 32, 27, 'Fe-500', 9011, 704, 11, 6, 7532, 19986)
(datetime.date(1995, 10, 19), datetime.time(13, 14), 'Hyderabad', 4158, Decimal('6.65'), 65, 'Rainy', '2DBE6340', 8, 7, 'Fe-550D', 3988, 925, 14, 6, 11513, 17307)
(datetime.date(1995, 10, 20), datetime.time(17, 37), 'Bengaluru', 432000, Decimal('7.71'), 56, 'Rainy', 'CECD6940', 16, 16, 'Fe-415D', 4928, 573, 4, 3, 6017, 23602)
(datetime.date(1995, 10, 21), datetime.time(12, 56), 'Hyderabad', 432000, Decimal('6.35'), 68, 'Rainy', 'CA109F33', 32, 27, 'Fe-550', 13392, 1287, 4, 2, 9535, 17930)
(datetime.date(1995, 10, 22), datetime.time(15, 43), 'Pune', 432000, Decimal('6.00'), 72, 'Rainy', '38EBC7C9', 10, 16, 'Fe-600', 13866, 1191, 14, 4, 7233, 24423)
(datetime.date(1995, 10, 23), datetime.time(11, 24), 'Hyderabad', 432000, Decimal('7.71'), 56, 'Rainy', '3E535FDC', 12, 16, 'Fe-500', 3183, 1278, 13, 2, 9089, 18949)
(datetime.date(1995, 10, 24), datetime.time(13, 59), 'Pune', 432000, Decimal('7.58'), 57, 'Rainy', '70A85E98', 10, 19, 'Fe-415D', 8600, 1110, 2, 6, 15000, 21419)
(datetime.date(1995, 10, 25), datetime.time(17, 23), 'Delhi', 444840, Decimal('15.10'), 64, None, 'B46D4D42', 10, 9, 'Fe-550', 4642, 1472, 2, 7, 7639, 22967)
(datetime.date(1995, 10, 26), datetime.time(17, 51), 'Pune', 443160, Decimal('6.61'), 67, 'Rainy', 'BB741E28', 32, 9, 'Fe-550', 11301, 876, 7, 3, 11668, 22201)
(datetime.date(1995, 10, 27), datetime.time(17, 9), 'Hyderabad', 448560, Decimal('7.01'), 64, 'Rainy', '9C02E51B', 8, 8, 'Fe-550D', 13025, 936, 12, 7, 10103, 21410)
(datetime.date(1995, 10, 28), datetime.time(12, 37), 'Pune', 446520, Decimal('6.38'), 70, 'Rainy', '85EE7F33', 32, 30, 'Fe-600', 14966, 952, 10, 1, 11892, 24563)
(datetime.date(1995, 10, 29), None, 'Hyderabad', 446400, Decimal('6.03'), 74, 'Rainy', 'DE595AA7', 10, 28, 'Fe-600D', 11698, 1279, 9, 2, 12195, 20922)
(datetime.date(1995, 10, 30), datetime.time(9, 48), 'Kolkata', 446400, Decimal('6.87'), 65, 'Rainy', '6C866A0D', 16, 30, 'Fe-500D', 8210, 1077, 12, 3, 8324, 17991)
(datetime.date(1995, 10, 31), datetime.time(13, 41), 'Hyderabad', 446280, Decimal('6.76'), 66, 'Rainy', 'C22459BD', 32, 17, 'Fe-550D', 4101, 508, 13, 6, 11334, 17972)
(datetime.date(1995, 11, 1), datetime.time(12, 34), None, 447000, Decimal('6.30'), 71, 'Rainy', '26D8DB1B', 32, 29, 'Fe-600D', 5909, 754, 3, 7, 10110, 23504)
(datetime.date(1995, 11, 2), datetime.time(9, 24), 'Hyderabad', 447960, Decimal('6.49'), 69, 'Rainy', '5388CFD0', 32, 11, 'Fe-600', 11039, 853, 12, 6, 12195, 18077)
(datetime.date(1995, 11, 3), datetime.time(11, 18), 'Kolkata', 447960, Decimal('7.00'), 64, 'Rainy', '2E38BBE1', 16, 26, 'Fe-550', 12894, 848, 1, 6, 13144, 15857)
(datetime.date(1995, 11, 4), datetime.time(11, 4), 'Kolkata', 447960, Decimal('7.00'), 64, 'Rainy', 'E7BD53A3', 12, 15, 'Fe-550D', 10593, 1441, 7, 4, 13142, 24087)
(datetime.date(1995, 11, 5), datetime.time(15, 15), 'Pune', 444000, Decimal('8.54'), 52, 'Rainy', '84366AD4', 16, 6, 'Fe-415', 13142, 937, 8, 3, 7682, 17764)
(datetime.date(1995, 11, 6), datetime.time(15, 45), 'Bengaluru', 450000, Decimal('7.26'), 62, 'Rainy', '616D23B2', 16, 20, 'Fe-550', 13330, 878, 8, 6, 12695, 18684)
(datetime.date(1995, 11, 7), datetime.time(14, 40), 'Hyderabad', 441000, Decimal('8.65'), 51, 'Rainy', '89AD9B77', 8, 7, 'Fe-415', 4094, 745, 4, 3, 10672, 23242)
(datetime.date(1995, 11, 8), datetime.time(9, 13), 'Hyderabad', 443880, Decimal('6.34'), 70, 'Rainy', '98955B8E', 10, 8, 'Fe-600D', 9979, 1349, 9, 2, 9731, 21426)
(datetime.date(1995, 11, 9), datetime.time(14, 23), 'Bengaluru', 438600, Decimal('4.10'), 63, 'Rainy', '9ED4EB33', 8, 9, 'Fe-550', 5947, 1242, 5, 5, 10180, 21622)
(datetime.date(1995, 11, 10), datetime.time(16, 57), 'Hyderabad', 438600, Decimal('6.85'), 64, 'Rainy', '6AE88AE3', 32, 6, 'Fe-500D', 7548, 683, 2, 2, 11861, 19600)
(datetime.date(1995, 11, 11), datetime.time(15, 53), 'Hyderabad', 438600, Decimal('6.85'), 64, 'Rainy', 'B7F846A1', 10, 11, 'Fe-550D', 11477, 984, 11, 7, 14030, 18310)
(datetime.date(1995, 11, 12), datetime.time(16, 38), 'Delhi', 438600, Decimal('7.69'), 57, 'Rainy', '6C9BCD17', 32, 15, 'Fe-500', 4295, 1366, 3, 1, 9727, 21706)
(datetime.date(1995, 11, 13), datetime.time(14, 43), 'Bengaluru', 438600, Decimal('6.18'), 71, 'Rainy', 'EAECE33D', 10, 28, 'Fe-600D', 14745, 1009, 2, 3, 10189, 15328)
(datetime.date(1995, 11, 14), datetime.time(11, 23), 'Pune', 438600, Decimal('8.60'), 51, 'Rainy', 'D33A7D89', 12, 29, 'Fe-415', 8960, 996, 12, 1, 8719, 18673)
(datetime.date(1995, 11, 15), datetime.time(13, 33), 'Kolkata', 438600, Decimal('7.97'), 55, 'Rainy', '2CD48ABA', 10, 14, 'Fe-415D', 4453, 558, 14, 3, 7269, 16343)
(datetime.date(1995, 11, 16), datetime.time(15, 32), 'Delhi', 435840, Decimal('5.97'), 73, 'Rainy', '5581DAB2', 12, 18, 'Fe-600D', 5967, 970, 10, 5, 12752, 19788)
(datetime.date(1995, 11, 17), datetime.time(12, 29), 'Delhi', 439320, Decimal('6.02'), 73, 'Rainy', '2E5F5294', 10, 25, 'Fe-600D', 4445, 823, 4, 5, 7177, 17436)
(datetime.date(1995, 11, 18), datetime.time(15, 18), 'Hyderabad', 434280, Decimal('5.87'), 74, 'Rainy', '81FE2A2E', 32, 17, 'Fe-600D', 3811, 1278, 11, 6, 9096, 16103)
(datetime.date(1995, 11, 19), datetime.time(9, 51), 'Bengaluru', 435600, Decimal('7.78'), 56, 'Rainy', '9369C396', 10, 21, 'Fe-415D', 3763, 1406, 6, 5, 10069, 19633)
(datetime.date(1995, 11, 20), datetime.time(14, 49), 'Delhi', 437400, Decimal('6.25'), 70, 'Rainy', '563E7E9D', 16, 28, 'Fe-600', 8197, 1488, 14, 5, 8705, 22171)
(datetime.date(1995, 11, 21), datetime.time(11, 32), 'Delhi', 438120, Decimal('6.26'), 70, 'Rainy', 'D9952CF8', 12, 8, 'Fe-600', 4410, 1461, 12, 3, 9395, 18650)
(datetime.date(1995, 11, 22), datetime.time(10, 30), 'Hyderabad', 438120, Decimal('6.85'), 64, 'Rainy', 'B1881D2E', 12, 22, 'Fe-500D', 8114, 1372, 6, 2, 9941, 15928)
(datetime.date(1995, 11, 23), datetime.time(12, 29), 'Delhi', 438120, Decimal('6.17'), 71, 'Rainy', 'CFD3C543', 16, 4, 'Fe-600', 3057, 663, 9, 6, 14347, 24494)
(datetime.date(1995, 11, 24), datetime.time(9, 18), 'Hyderabad', 443400, Decimal('8.37'), None, 'Rainy', '0BFF5416', 10, 9, 'Fe-415', 10730, 703, 14, 5, 10486, 16443)
(datetime.date(1995, 11, 25), datetime.time(11, 30), 'Hyderabad', 441000, Decimal('7.88'), 56, 'Rainy', '343A3479', 10, 28, 'Fe-415D', 3688, 1365, 6, 3, 9354, 21437)
(datetime.date(1995, 11, 26), datetime.time(16, 42), 'Pune', 442440, Decimal('6.51'), 68, 'Rainy', '6E5E8BD1', 10, 10, 'Fe-550', 14836, 993, 12, 6, 12077, 16839)
(datetime.date(1995, 11, 27), datetime.time(16, 54), 'Bengaluru', 443760, Decimal('7.52'), 59, 'Rainy', 'E8288B89', 10, 20, 'Fe-500', 5002, 1381, 14, 7, 11290, 24738)
(datetime.date(1995, 11, 28), datetime.time(11, 6), 'Pune', 444840, Decimal('6.84'), 65, 'Rainy', 'E5B34474', 8, 12, 'Fe-550D', 14894, 855, 6, 5, 13390, 23287)
(datetime.date(1995, 11, 29), datetime.time(17, 54), 'Bengaluru', 4158, Decimal('6.28'), 71, 'Rainy', 'F37CC9C8', 32, 15, 'Fe-600D', 8684, 770, 3, 3, 11967, 21327)
(datetime.date(1995, 11, 30), datetime.time(17, 37), 'Delhi', 450480, Decimal('15.20'), 63, 'Rainy', 'D17C9B72', 16, 18, 'Fe-550', 8957, 849, 14, 4, 7372, 17786)
(datetime.date(1995, 12, 1), datetime.time(11, 41), 'Kolkata', 455520, Decimal('8.59'), 53, 'Rainy', '01E88151', 8, 28, 'Fe-415', 13783, 886, 5, 5, 13383, 20141)
(datetime.date(1995, 12, 2), datetime.time(15, 34), 'Hyderabad', 454560, Decimal('7.70'), 59, 'Rainy', 'A5734569', 10, 11, 'Fe-500', 8370, 1372, 3, 1, 11359, 20218)
(datetime.date(1995, 12, 3), datetime.time(11, 3), 'Bengaluru', 455400, Decimal('7.59'), 60, 'Rainy', '00EB9862', 16, 14, 'Fe-500D', 5298, 796, 10, 1, 13634, 23601)
(datetime.date(1995, 12, 4), datetime.time(13, 59), 'Delhi', 456240, Decimal('6.52'), 70, 'Rainy', '2DE3998E', 8, 11, 'Fe-600D', 10966, 662, 3, 2, 13341, 22686)
(datetime.date(1995, 12, 5), datetime.time(9, 24), 'Pune', 457080, Decimal('8.46'), 54, 'Rainy', 'D296759D', 16, 27, 'Fe-415D', 6697, 962, 1, 4, 8087, 19181)
(datetime.date(1995, 12, 6), datetime.time(12, 35), 'Kolkata', 459120, Decimal('7.92'), 58, 'Rainy', '9AE4AFCB', 8, 32, 'Fe-415D', 13029, 1221, 8, 6, 8038, 22498)
(datetime.date(1995, 12, 7), datetime.time(16, 37), 'Pune', 459720, Decimal('4.20'), 73, 'Rainy', '0D753AE9', 10, 14, 'Fe-600D', 6675, 774, 2, 2, 6018, 21083)
(datetime.date(1995, 12, 8), datetime.time(9, 44), 'Bengaluru', 460320, Decimal('6.77'), 68, 'Rainy', '654062AD', 10, 13, 'Fe-550D', 13609, 761, 15, 6, 8525, 23757)
(datetime.date(1995, 12, 9), datetime.time(11, 4), 'Bengaluru', 456240, Decimal('6.34'), 72, 'Rainy', '31653D46', 16, 26, 'Fe-600', 7344, 1429, 10, 3, 13774, 15058)
(datetime.date(1995, 12, 10), datetime.time(11, 42), 'Hyderabad', 444960, Decimal('6.64'), 67, 'Rainy', '6E69CE92', 10, 14, 'Fe-550', 6972, 1494, 13, 2, 7890, 17879)
(datetime.date(1995, 12, 11), datetime.time(14, 48), 'Delhi', 449280, Decimal('7.37'), 61, 'Rainy', '74E6DD51', 10, 23, 'Fe-500D', 8937, 1287, 12, 6, 10870, 20551)
(datetime.date(1995, 12, 12), datetime.time(15, 12), 'Kolkata', 453600, Decimal('7.09'), 64, 'Rainy', '35712A3F', 32, 27, 'Fe-500D', 7525, 1066, 3, 1, 12450, 21098)
(datetime.date(1995, 12, 13), datetime.time(14, 12), 'Pune', 452520, Decimal('7.30'), 62, 'Rainy', '2797F1DF', 8, 31, 'Fe-500', 8457, 1165, 2, 4, 13102, 20954)
(datetime.date(1995, 12, 14), datetime.time(13, 41), 'Delhi', 452520, Decimal('7.54'), 60, 'Rainy', '62F71FFF', 10, 27, 'Fe-500', 3171, 1012, 11, 1, 10950, 23166)
(datetime.date(1995, 12, 15), datetime.time(17, 25), 'Pune', 452520, Decimal('8.70'), 52, 'Rainy', 'ED6A886A', 10, 25, 'Fe-415', 11190, 894, 11, 4, 10885, 17995)
(datetime.date(1995, 12, 16), datetime.time(17, 5), 'Delhi', 452520, Decimal('6.37'), 71, 'Rainy', '408389BA', 10, 14, 'Fe-600', 10024, 971, 5, 3, 8235, 17009)
(datetime.date(1995, 12, 17), datetime.time(15, 23), 'Delhi', 452520, Decimal('7.80'), 58, 'Rainy', '55E3C6DE', 12, 26, 'Fe-415D', 7276, 798, 9, 2, 8152, 18117)
(datetime.date(1995, 12, 18), datetime.time(17, 13), 'Pune', 452520, Decimal('7.94'), 57, 'Rainy', 'E316FAF3', 16, 9, 'Fe-500', 6705, 754, 9, 4, 9504, 18397)
(datetime.date(1995, 12, 19), datetime.time(9, 54), 'Bengaluru', 452520, Decimal('7.07'), 64, 'Rainy', '3411F5D8', 12, 27, 'Fe-550D', 8346, 1020, 6, 7, 15000, 24324)
(datetime.date(1995, 12, 20), datetime.time(13, 43), 'Pune', 451200, Decimal('7.65'), 59, 'Rainy', '28CD5F50', 10, 6, 'Fe-500', 8288, 1276, 1, 6, 12286, 24521)
(datetime.date(1995, 12, 21), datetime.time(9, 2), 'Hyderabad', 446160, Decimal('6.11'), 73, 'Rainy', '4758D598', None, 27, 'Fe-600D', 4416, 539, 6, 5, 14266, 19077)
(datetime.date(1995, 12, 22), datetime.time(11, 7), 'Delhi', 446160, Decimal('7.20'), 62, 'Rainy', '381AD177', 12, 32, 'Fe-500', 8063, 1380, 6, 5, 8664, 22679)
(datetime.date(1995, 12, 23), datetime.time(13, 41), 'Kolkata', 446160, Decimal('7.97'), 56, 'Rainy', 'C7B4299D', 10, 24, 'Fe-500', 5326, 581, 8, 6, 7704, 22113)
(datetime.date(1995, 12, 24), datetime.time(15, 3), 'Delhi', 446160, Decimal('6.56'), 68, 'Rainy', '93659350D', 32, 17, 'Fe-550D', 3829, 792, 6, 1, 13841, 17378)
(datetime.date(1995, 12, 25), datetime.time(13, 23), 'Pune', 446160, Decimal('8.11'), 55, 'Rainy', 'E2FE96ED', 16, 11, 'Fe-415D', 7625, 820, 13, 7, 13700, 22656)
(datetime.date(1995, 12, 26), datetime.time(14, 4), 'Bengaluru', 446160, Decimal('7.44'), 60, 'Rainy', 'C36B6D96', 32, 12, 'Fe-500', 14441, 778, 11, 2, 11638, 19219)
(datetime.date(1995, 12, 27), datetime.time(11, 18), 'Hyderabad', 454920, Decimal('6.69'), 68, 'Rainy', 'AE549ED2', 12, 12, 'Fe-550D', 8482, 611, 2, 6, 8891, 21535)
(datetime.date(1995, 12, 28), datetime.time(9, 31), 'Kolkata', 452640, Decimal('6.66'), 68, 'Rainy', 'D15AB778', 8, 16, 'Fe-600', 4918, 563, 9, 2, 11035, 18880)
(datetime.date(1995, 12, 29), datetime.time(11, 14), 'Pune', 453840, Decimal('6.39'), 71, 'Rainy', 'F62A0865', 12, 9, 'Fe-600D', 14625, 1294, 3, 5, 13525, 21869)
(datetime.date(1995, 12, 30), datetime.time(10, 54), 'Delhi', 448440, Decimal('8.01'), 56, 'Rainy', 'BD1C6DAC', 8, 16, 'Fe-500', 10884, 1092, 12, 5, 8958, 21652)
(datetime.date(1995, 12, 31), datetime.time(9, 22), 'Hyderabad', 448920, Decimal('7.24'), 62, 'Rainy', 'D1CF83D3', 10, 7, 'Fe-550', 3926, 822, 9, 5, 14400, 18498)
(datetime.date(1996, 1, 1), datetime.time(16, 42), 'Delhi', 449280, Decimal('6.15'), 73, 'Rainy', '9F22866D', 16, 6, None, 4042, 932, 5, 5, 7215, 22737)
(datetime.date(1996, 1, 2), datetime.time(12, 19), 'Delhi', 449760, Decimal('6.61'), 68, 'Rainy', '6CD652DB', 12, 19, 'Fe-550', 7587, 1405, 13, 1, 10035, 24914)
(datetime.date(1996, 1, 3), datetime.time(16, 10), 'Bengaluru', 449520, Decimal('6.42'), 70, 'Rainy', 'B7EBA3DD', 8, 16, 'Fe-600D', 3400, 1235, 9, 5, 7278, 20373)
(datetime.date(1996, 1, 4), datetime.time(14, 12), 'Bengaluru', 448080, Decimal('4.30'), 74, 'Rainy', 'EA724FDA', 12, 28, 'Fe-600D', 13292, 1206, 4, 7, 7880, 21578)
(datetime.date(1996, 1, 5), datetime.time(17, 56), 'Kolkata', 448200, Decimal('6.90'), 65, 'Rainy', 'A7E3572B', 10, 23, 'Fe-550D', 7470, 778, 5, 6, 14358, 19586)
(datetime.date(1996, 1, 6), datetime.time(11, 25), 'Delhi', 447120, Decimal('6.12'), 73, 'Rainy', 'E74836FB', 16, 30, 'Fe-600D', 13645, 1054, 1, 5, 11585, 15947)
(datetime.date(1996, 1, 7), datetime.time(10, 26), 'Pune', 449040, Decimal('6.91'), 65, 'Rainy', '12ED33A2', 8, 23, 'Fe-550', 4529, 557, 5, 2, 12259, 20913)
(datetime.date(1996, 1, 8), datetime.time(15, 21), 'Pune', 450840, Decimal('4.50'), 55, 'Rainy', '5C47F868', 16, 22, 'Fe-415', 12642, 1045, 14, 6, 7837, 23660)
(datetime.date(1996, 1, 9), datetime.time(13, 13), 'Bengaluru', 452760, Decimal('6.29'), 72, 'Rainy', '7AB549BE', 10, 28, 'Fe-600D', 5589, 1419, 11, 5, 7660, 23646)
(datetime.date(1996, 1, 10), datetime.time(10, 37), 'Kolkata', 451200, Decimal('8.85'), 51, 'Rainy', '1CDD67E7', 12, 19, 'Fe-415', 9013, 1187, 2, 6, 13411, 19424)
(datetime.date(1996, 1, 11), datetime.time(16, 5), 'Delhi', 450840, Decimal('6.44'), 70, 'Rainy', '775FD211', 32, 26, 'Fe-600D', 12996, 617, 4, 4, 9828, 24740)
(datetime.date(1996, 1, 12), datetime.time(14, 22), 'Bengaluru', 457800, Decimal('6.83'), 67, 'Rainy', '4A1454D9', 10, 15, 'Fe-550D', 10625, 826, 8, 7, 8974, 21526)
(datetime.date(1996, 1, 13), datetime.time(9, 4), 'Delhi', 451800, Decimal('6.74'), 67, 'Rainy', '44314C3B', 12, 4, 'Fe-550', 6070, 1100, 14, 6, 11605, 23726)
(datetime.date(1996, 1, 14), datetime.time(12, 13), 'Kolkata', 452280, Decimal('6.11'), 74, 'Rainy', 'B9E13EA7', 16, 12, 'Fe-600D', 14039, 1267, 7, 7, 9477, 21720)
(datetime.date(1996, 1, 15), datetime.time(9, 41), 'Kolkata', 452880, Decimal('8.88'), 51, 'Rainy', 'A818B87E', 8, 9, 'Fe-415', 5024, 924, 6, 5, 13620, 21784)
(datetime.date(1996, 1, 16), datetime.time(10, 47), 'Hyderabad', 453360, Decimal('6.77'), 67, 'Rainy', '23EA71C4', 8, 13, 'Fe-550D', 7477, 888, 12, 2, 14297, 21689)
(datetime.date(1996, 1, 17), datetime.time(13, 55), 'Delhi', 453360, Decimal('6.87'), 66, 'Rainy', '94B3E7EE', 16, 19, 'Fe-550D', 3241, 526, 3, 3, 8916, 23314)
(datetime.date(1996, 1, 18), datetime.time(17, 49), 'Pune', 452520, Decimal('6.96'), 65, 'Rainy', '6E18B7B2', 12, 5, 'Fe-550D', 10887, 728, 13, 3, 13468, 19617)
(datetime.date(1996, 1, 19), datetime.time(14, 16), 'Hyderabad', 445200, Decimal('6.85'), 65, 'Rainy', '5DAC52AB', 12, 22, 'Fe-550D', 10920, 1376, 8, 4, 9659, 18664)
(datetime.date(1996, 1, 20), datetime.time(10, 18), 'Kolkata', 447360, Decimal('6.13'), 73, 'Rainy', 'D1AE2CFB', 10, 15, 'Fe-600D', 13466, 671, 13, 3, 10084, 18072)
(datetime.date(1996, 1, 21), datetime.time(10, 30), 'Bengaluru', 452160, Decimal('6.75'), 67, 'Rainy', 'BCE55314', 10, 25, 'Fe-550D', 11632, 593, 8, 1, 9541, 24033)
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(datetime.date(1996, 4, 1), datetime.time(13, 1), 'Kolkata', 492720, Decimal('7.47'), 66, 'Winter', '5C89CE5C', 32, 31, 'Fe-550D', 7368, 515, 7, 2, 10968, 20930)
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(datetime.date(1996, 4, 3), datetime.time(15, 49), 'Hyderabad', 488520, Decimal('7.88'), 62, 'Winter', 'D9ACA4EE', 10, 15, 'Fe-500', 8728, 557, 6, 1, 9886, 19924)
(datetime.date(1996, 4, 4), datetime.time(9, 23), 'Pune', 490080, Decimal('7.66'), 64, 'Winter', '781BB88C', 8, 23, 'Fe-500D', 10331, 513, 6, 5, 12385, 15163)
(datetime.date(1996, 4, 5), datetime.time(11, 21), 'Delhi', 486360, Decimal('6.85'), 71, 'Winter', '81F1306F', 32, 30, 'Fe-600', 8315, 994, 13, 3, 12951, 19058)
(datetime.date(1996, 4, 6), datetime.time(17, 41), 'Hyderabad', 487200, Decimal('7.73'), 63, 'Winter', '82E26780', 10, 23, 'Fe-500D', 10585, 1305, 5, 6, 14347, 21113)
(datetime.date(1996, 4, 7), datetime.time(13, 49), 'Bengaluru', 492600, Decimal('7.95'), 62, 'Winter', '64EB32BA', 32, 18, 'Fe-550', 11049, 522, 5, 4, 14865, 21074)
(datetime.date(1996, 4, 8), datetime.time(17, 3), 'Pune', 4162, Decimal('9.96'), 50, 'Winter', 'AEB1F8D5', 8, 20, 'Fe-415', 13497, 1486, 10, 5, 11911, 18585)
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(datetime.date(1996, 4, 11), datetime.time(11, 28), 'Kolkata', 510960, Decimal('9.46'), 54, 'Winter', 'ED57B431', 32, 11, 'Fe-415', 5380, 1453, 8, 6, 13910, 23975)
(datetime.date(1996, 4, 12), datetime.time(12, 24), 'Hyderabad', 510960, Decimal('8.24'), 62, 'Winter', 'D3B1B31D', 12, 24, 'Fe-550', 8598, 1216, 14, 3, 11141, 23531)
(datetime.date(1996, 4, 13), datetime.time(15, 40), 'Delhi', 510960, Decimal('8.11'), 63, 'Winter', 'D0985D53', 8, 20, 'Fe-550', 9205, 738, 9, 5, 9942, 17499)
(datetime.date(1996, 4, 14), datetime.time(10, 17), 'Delhi', 510960, Decimal('9.46'), 54, 'Winter', 'BFC83E5D', 8, 23, 'Fe-415', 11069, 912, 3, 2, 10736, 17662)
(datetime.date(1996, 4, 15), datetime.time(9, 30), 'Hyderabad', 510960, Decimal('9.64'), 53, 'Winter', '7784BA84', 8, 25, 'Fe-415', 6753, 1294, 10, 5, 7839, 18633)
(datetime.date(1996, 4, 16), datetime.time(15, 13), 'Bengaluru', 510960, Decimal('7.51'), 68, 'Winter', '4D2AE84F', 32, 29, 'Fe-600', 13832, 894, 15, 5, 8832, 16301)
(datetime.date(1996, 4, 17), datetime.time(9, 57), 'Pune', 510960, Decimal('7.74'), 66, 'Winter', '526249C0', 32, 19, 'Fe-550D', 7483, 514, 10, 7, 13364, 15851)
(datetime.date(1996, 4, 18), datetime.time(13, 39), 'Kolkata', 510960, Decimal('9.64'), 53, 'Winter', '55E2F5EE', 12, 20, 'Fe-415', 3726, 1176, 9, 4, 11603, 15308)
(datetime.date(1996, 4, 19), datetime.time(12, 5), 'Pune', 528000, Decimal('8.95'), 59, 'Winter', 'DE69BB43', 8, 29, 'Fe-500', 12732, 947, 6, 3, 8783, 18749)
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(datetime.date(1996, 4, 21), datetime.time(16, 30), 'Pune', 528000, Decimal('8.95'), 59, 'Winter', '547279CF', 12, 16, 'Fe-500', 7860, 748, 6, 7, 9171, 16192)
(datetime.date(1996, 4, 22), datetime.time(11, 58), 'Kolkata', 528000, Decimal('9.60'), 55, 'Winter', '29144023', 10, 23, 'Fe-415', 4190, 994, 1, 6, 13003, 16881)
(datetime.date(1996, 4, 23), datetime.time(10, 58), 'Pune', 528000, Decimal('7.33'), 72, 'Winter', 'D6818549', 12, 28, 'Fe-600', 5869, 940, 10, 6, 11709, 20424)
(datetime.date(1996, 4, 24), datetime.time(12, 22), 'Pune', 528000, Decimal('7.88'), 67, 'Winter', '734FC3A4', 32, 7, 'Fe-550', 10171, 964, 11, 2, 10169, 17830)
(datetime.date(1996, 4, 25), datetime.time(17, 18), 'Kolkata', 504000, Decimal('9.33'), 54, 'Winter', 'C221B4AD', 10, 28, 'Fe-415D', 3972, 602, 5, 4, 9435, 15765)
(datetime.date(1996, 4, 26), datetime.time(12, 17), 'Delhi', None, Decimal('7.86'), 65, 'Winter', 'E2360696', 10, 28, 'Fe-550D', 11798, 555, 4, 6, 8852, 16012)
(datetime.date(1996, 4, 27), datetime.time(11, 3), 'Pune', 519360, Decimal('8.66'), 60, 'Winter', 'D2668BBE', 10, 28, 'Fe-500', 14256, 1221, 15, 5, 7814, 20360)
(datetime.date(1996, 4, 28), datetime.time(17, 6), 'Kolkata', 524640, Decimal('9.37'), 56, 'Winter', '1D6A1E63', 16, 6, 'Fe-415D', 6363, 950, 12, 3, 7539, 24341)
(datetime.date(1996, 4, 29), datetime.time(15, 14), 'Kolkata', 530040, Decimal('7.91'), 67, 'Winter', '7A2A51CC', 16, 23, 'Fe-550D', 13139, 1406, 2, 2, 10621, 19009)
(datetime.date(1996, 4, 30), datetime.time(12, 9), 'Delhi', 535320, Decimal('8.63'), 62, 'Winter', 'C7935D50', 12, 15, 'Fe-550', 13365, 1210, 6, 7, 8570, 22426)
(datetime.date(1996, 5, 1), datetime.time(17, 14), 'Bengaluru', 545520, Decimal('7.91'), 69, 'Winter', 'C8E25234', 12, 19, 'Fe-600', 14415, 1415, 4, 7, 12111, 17418)
(datetime.date(1996, 5, 2), datetime.time(14, 4), 'Pune', 523920, Decimal('8.06'), 65, 'Winter', '5EBC9DFC', 12, 27, 'Fe-550D', 6344, 1366, 9, 4, 11681, 17228)
(datetime.date(1996, 5, 3), datetime.time(17, 5), 'Pune', 511560, Decimal('8.53'), 60, 'Winter', '26061EDE', 12, 23, 'Fe-500', 8131, 1400, 6, 6, 12812, 23469)
(datetime.date(1996, 5, 4), datetime.time(11, 7), 'Pune', 505080, Decimal('7.11'), 71, 'Winter', 'A6C7A2D8', 32, 7, 'Fe-600D', 7360, 954, 8, 6, 7020, 20311)
(datetime.date(1996, 5, 5), datetime.time(9, 1), 'Pune', 505680, Decimal('7.33'), 69, 'Winter', '414B5579', 10, 30, 'Fe-600', 10968, 1367, 9, 4, 13137, 24172)
(datetime.date(1996, 5, 6), datetime.time(11, 45), 'Kolkata', 506160, Decimal('7.91'), 64, 'Winter', 'DB489B4C', 8, 28, 'Fe-550D', 12503, 1014, 12, 2, 8514, 24960)
(datetime.date(1996, 5, 7), datetime.time(10, 5), 'Bengaluru', 506760, Decimal('7.14'), 71, 'Winter', 'DA62EEEA', 16, 10, 'Fe-600D', 7840, 617, 2, 2, 11619, 22019)
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(datetime.date(1996, 12, 16), datetime.time(13, 25), 'Delhi', 640200, Decimal('8.77'), 73, 'Rainy', '31E6FCC6', 16, 27, 'Fe-600D', 8937, 1317, 10, 7, 12782, 18467)
(datetime.date(1996, 12, 17), datetime.time(10, 14), 'Delhi', 636960, Decimal('10.44'), 61, 'Rainy', 'A74BBA85', 10, 24, 'Fe-500', 3276, 883, 6, 3, 7830, 21632)
(datetime.date(1996, 12, 18), datetime.time(10, 55), 'Hyderabad', 637320, Decimal('8.85'), 72, 'Rainy', 'E86CB521', 16, 17, 'Fe-600', 6581, 530, 10, 5, 13896, 16555)
(datetime.date(1996, 12, 19), datetime.time(16, 5), 'Kolkata', 642360, Decimal('9.18'), 70, 'Rainy', '7BE61248', 12, 14, 'Fe-600D', 6354, 852, 7, 5, 10555, 15639)
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(datetime.date(1996, 12, 21), datetime.time(17, 53), 'Delhi', 641160, Decimal('11.25'), 57, 'Rainy', '82611D3E', 12, 31, 'Fe-500', 11206, 1004, 9, 5, 14230, 20646)
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(datetime.date(1996, 12, 26), datetime.time(10, 32), 'Delhi', 615720, Decimal('11.19'), 55, 'Rainy', '5B716424', 8, 9, 'Fe-415', 12868, 771, 2, 3, 8252, 18899)
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(datetime.date(1996, 12, 31), datetime.time(15, 43), 'Delhi', 608280, Decimal('9.08'), 67, 'Rainy', 'CCBB1BC9', 10, 8, 'Fe-550', 7841, 1315, 6, 5, 10957, 20183)
(datetime.date(1997, 1, 1), datetime.time(13, 41), 'Hyderabad', 617160, Decimal('9.08'), 68, 'Rainy', '2FD96278', 10, 9, 'Fe-550', 9691, 519, 14, 5, 12650, 22538)
(datetime.date(1997, 1, 2), None, 'Kolkata', 619080, Decimal('8.72'), 71, 'Rainy', '2972231B', 12, 4, 'Fe-600', 14456, 1246, 2, 7, 7154, 21866)
(datetime.date(1997, 1, 3), datetime.time(16, 21), 'Pune', 616320, Decimal('8.68'), 71, 'Rainy', 'E4BE19B9', 12, 14, 'Fe-600', 8921, 1183, 7, 1, 11772, 18266)
(datetime.date(1997, 1, 4), datetime.time(9, 44), 'Kolkata', 608880, Decimal('10.68'), 57, 'Rainy', 'B12B40EE', 16, 19, 'Fe-500', 7088, 983, 13, 4, 14408, 18383)
(datetime.date(1997, 1, 5), datetime.time(15, 9), 'Hyderabad', 614280, Decimal('8.90'), 69, 'Rainy', '798D3490', 32, 26, 'Fe-600', 10920, 1475, 13, 3, 7434, 20739)
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(datetime.date(1997, 1, 7), datetime.time(12, 48), 'Hyderabad', 624960, Decimal('11.79'), 53, 'Rainy', '2E4233D7', 32, 16, 'Fe-415', 3545, 908, 7, 3, 11709, 17832)
(datetime.date(1997, 1, 8), datetime.time(10, 42), 'Kolkata', 627960, Decimal('11.85'), 53, 'Rainy', '8D68CD7F', 8, 7, 'Fe-415', 6721, 825, 4, 2, 11774, 17627)
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(datetime.date(1997, 2, 14), datetime.time(13, 48), 'Delhi', 709680, Decimal('13.14'), 54, 'Winter', '41997911', 12, 5, 'Fe-415', 5209, 1009, 4, 5, 8699, 20157)
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(datetime.date(1997, 2, 16), datetime.time(16, 7), 'Bengaluru', 710880, Decimal('13.41'), 53, 'Winter', 'D4AB5312', 12, 7, 'Fe-415', 10697, 615, 10, 7, 14457, 16649)
(datetime.date(1997, 2, 17), datetime.time(13, 59), 'Hyderabad', 710760, Decimal('9.87'), 72, 'Winter', '1E2D0559', 12, 16, 'Fe-600', 13006, 1371, 6, 3, 11653, 20510)
(datetime.date(1997, 2, 18), datetime.time(9, 2), 'Bengaluru', 710640, Decimal('10.93'), 65, 'Winter', 'C1D315F8', 16, 25, 'Fe-550D', 10530, 709, 5, 5, 8395, 18583)
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(datetime.date(1997, 2, 22), datetime.time(14, 28), 'Bengaluru', 682440, Decimal('13.65'), 50, 'Winter', 'B495188E', 8, 12, 'Fe-415', 9970, 877, 1, 7, 14016, 24310)
(datetime.date(1997, 2, 23), datetime.time(12, 1), 'Hyderabad', 678240, Decimal('10.94'), 62, 'Winter', '6E7A12C4', 10, 26, 'Fe-500D', 12471, 618, 8, 3, 13127, 18311)
(datetime.date(1997, 2, 24), datetime.time(12, 40), 'Pune', 674160, Decimal('10.87'), 62, 'Winter', 'ACDDA186', 12, 9, 'Fe-500D', 3398, 981, 6, 5, 11248, 23395)
(datetime.date(1997, 2, 25), datetime.time(10, 35), 'Bengaluru', 669960, Decimal('11.96'), 56, 'Winter', '9C32E335', 32, 28, 'Fe-415D', 9579, 1342, 3, 4, 7862, 15694)
(datetime.date(1997, 2, 26), datetime.time(16, 34), 'Delhi', 670776, Decimal('15.60'), 52, 'Winter', '22D19A72', 8, 13, 'Fe-415', 14206, 1354, 5, 5, 9727, 16869)
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(datetime.date(1997, 2, 28), datetime.time(10, 30), 'Bengaluru', 641856, Decimal('10.70'), 60, 'Winter', '1FCE3F43', 10, 17, 'Fe-500', 13287, 731, 14, 3, 12697, 19287)
(datetime.date(1997, 3, 1), datetime.time(13, 9), 'Kolkata', 632028, Decimal('9.03'), 70, 'Winter', 'D214BD6B', 10, 14, 'Fe-600', 4251, 687, 14, 2, 11980, 15535)
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(datetime.date(1997, 3, 3), datetime.time(14, 10), 'Hyderabad', 609000, Decimal('8.34'), 73, 'Winter', '5630AEDB', 12, 23, 'Fe-600D', 8221, 545, 14, 4, 12259, 24536)
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(datetime.date(1997, 3, 5), datetime.time(10, 57), 'Bengaluru', 592548, Decimal('9.41'), 63, 'Winter', '93AEE965', 32, 31, 'Fe-500D', 8593, 1111, 8, 3, 14722, 17956)
(datetime.date(1997, 3, 6), datetime.time(15, 52), 'Hyderabad', 570432, Decimal('8.03'), 71, 'Winter', '51364A8D', 16, 22, 'Fe-600D', 7785, 735, 1, 2, 7113, 18057)
(datetime.date(1997, 3, 7), datetime.time(12, 38), 'Pune', 572388, Decimal('8.06'), 71, 'Winter', '942739F7', 12, 16, 'Fe-600D', 4526, 911, 6, 6, 13710, 21529)
(datetime.date(1997, 3, 8), datetime.time(15, 47), 'Bengaluru', 580848, Decimal('8.07'), 72, 'Winter', '3319C2A0', 12, 12, 'Fe-600', 13817, 1267, 15, 3, 7621, 19124)
(datetime.date(1997, 3, 9), datetime.time(9, 46), 'Delhi', 579840, Decimal('11.37'), 51, 'Winter', '52DB22DD', 10, 23, 'Fe-415', 4755, 1402, 14, 6, 14183, 18090)
(datetime.date(1997, 3, 10), datetime.time(11, 17), 'Kolkata', 565224, Decimal('8.83'), 64, 'Winter', 'F9A6BD94', 16, 15, 'Fe-550D', 5325, 847, 4, 6, 7048, 15414)
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(datetime.date(1997, 3, 12), datetime.time(13, 9), 'Bengaluru', 540504, Decimal('9.65'), 56, 'Winter', 'D716C6F7', 32, 10, 'Fe-500', 3950, 722, 9, 5, 8552, 23240)
(datetime.date(1997, 3, 13), datetime.time(13, 54), 'Bengaluru', 541152, Decimal('8.87'), 61, 'Winter', 'EEBB0675', 12, 15, 'Fe-500', 4228, 1049, 7, 3, 7618, 19385)
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(datetime.date(1997, 3, 17), datetime.time(9, 46), 'Bengaluru', 538920, Decimal('7.70'), 70, 'Winter', '92768D86', 32, 20, 'Fe-600', 9876, 1479, 9, 3, 12044, 18391)
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(datetime.date(1997, 3, 21), datetime.time(13, 51), 'Hyderabad', 522504, Decimal('7.46'), 70, 'Winter', '5F95AC44', 32, 6, 'Fe-600', 6055, 874, 5, 5, 12521, 24684)
(datetime.date(1997, 3, 22), datetime.time(11, 15), 'Bengaluru', 528864, Decimal('9.28'), 57, 'Winter', 'ACF8C75B', 8, 10, 'Fe-500', 14797, 633, 7, 4, 14601, 15100)
(datetime.date(1997, 3, 23), datetime.time(11, 19), 'Delhi', 528960, Decimal('8.67'), 61, 'Winter', '88413809', 12, 30, 'Fe-500', 4374, 987, 4, 5, 14241, 19731)
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(datetime.date(1997, 3, 25), datetime.time(16, 51), 'Hyderabad', 523860, Decimal('8.88'), 59, 'Winter', '9489E6E1', 32, 27, 'Fe-500', 12900, 1399, 3, 2, 8253, 18643)
(datetime.date(1997, 3, 26), datetime.time(9, 28), 'Kolkata', 521136, Decimal('7.04'), 74, 'Winter', 'EDE21B74', 32, 30, 'Fe-600D', 6474, 957, 13, 4, 14717, 17667)
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(datetime.date(1998, 2, 18), None, 'Pune', 466680, Decimal('8.81'), 53, 'Winter', 'DB725662', 16, 17, 'Fe-415', 3770, 1132, 10, 7, 9490, 21607)
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(datetime.date(1999, 12, 5), datetime.time(10, 27), 'Bengaluru', 477016, Decimal('7.80'), 63, 'Winter', 'D5C07499', 22, 22, 'Fe-550D', 6897, 1236, 14, 5, 11305, 21416)
(datetime.date(1999, 12, 6), datetime.time(14, 23), 'Delhi', 457161, Decimal('7.81'), 63, 'Rainy', 'B2962DDD', 20, 31, 'Fe-600D', 5799, 1333, 12, 3, 12813, 22102)
(datetime.date(1999, 12, 7), datetime.time(14, 4), 'Bengaluru', 580769, Decimal('7.94'), 74, 'Rainy', 'CE573A11', 9, 16, 'Fe-550', 7876, 613, 10, 2, 11651, 20596)
(datetime.date(1999, 12, 8), datetime.time(10, 14), 'Delhi', 466158, Decimal('7.50'), 57, 'Winter', '00EB9862', 22, 28, 'Fe-600', 13590, 1326, 10, 3, 7824, 23123)
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(datetime.date(1999, 12, 11), datetime.time(13, 43), 'Kolkata', 467559, Decimal('6.81'), 70, 'Winter', 'EFCBAB93', 19, 29, 'Fe-500', 11968, 922, 8, 1, 13990, 18083)
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(datetime.date(1999, 12, 15), datetime.time(14, 21), 'Hyderabad', 639932, Decimal('11.63'), 60, 'Winter', '6A4AEA94', 8, 17, 'Fe-600D', 13510, 1299, 6, 6, 13998, 21171)
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(datetime.date(1999, 12, 24), datetime.time(11, 13), 'Hyderabad', 651386, Decimal('11.58'), 55, 'Winter', '7E8611B7', 30, 9, 'Fe-500', 14644, 1025, 12, 2, 9132, 20537)
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(datetime.date(2000, 1, 1), datetime.time(9, 2), 'Bengaluru', 561531, Decimal('7.88'), 73, 'Rainy', '2972231B', 29, 28, 'Fe-415', 11928, 568, 7, 2, 13917, 22838)
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(datetime.date(2000, 2, 1), datetime.time(13, 1), 'Kolkata', 581476, Decimal('9.99'), 54, 'Winter', '3394D956', 11, 23, 'Fe-415', 4140, 1470, 11, 1, 13805, 23996)
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(datetime.date(2000, 2, 6), datetime.time(17, 53), 'Delhi', 471826, Decimal('7.94'), 60, 'Winter', '8ECE482A', 16, 29, 'Fe-600', 13330, 1382, 12, 3, 9888, 17132)
(datetime.date(2000, 2, 7), datetime.time(15, 10), 'Bengaluru', 474407, Decimal('9.02'), 53, 'Winter', '4AFAA5AC', 22, 18, 'Fe-415', 4754, 823, 5, 1, 12406, 18176)
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(datetime.date(2000, 2, 9), datetime.time(15, 33), 'Bengaluru', 566292, Decimal('9.38'), 61, 'Winter', 'D865962C', 15, 16, 'Fe-500', 5913, 879, 13, 1, 8922, 15044)
(datetime.date(2000, 2, 10), datetime.time(16, 21), 'Hyderabad', 556510, Decimal('9.54'), 60, 'Winter', 'F8A17F65', 10, 17, 'Fe-415D', 9951, 1161, 13, 5, 13804, 18847)
(datetime.date(2000, 2, 11), datetime.time(11, 35), 'Kolkata', 551269, Decimal('7.95'), 68, 'Winter', 'C19FF7ED', 14, 25, 'Fe-500', 6845, 507, 3, 5, 14920, 24200)
(datetime.date(2000, 2, 12), datetime.time(15, 16), 'Hyderabad', 609266, Decimal('11.05'), 51, 'Winter', 'F62E0AC1', 16, 9, 'Fe-600', 6014, 1432, 4, 6, 14085, 23489)
(datetime.date(2000, 2, 13), datetime.time(13, 39), 'Hyderabad', 425117, Decimal('5.88'), 73, 'Winter', '798D3490', 9, 12, 'Fe-500D', 12610, 1225, 12, 3, 7102, 21704)
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(datetime.date(2000, 2, 15), datetime.time(13, 35), 'Pune', 531945, Decimal('8.95'), 61, 'Winter', '0921C57C', 12, 9, 'Fe-600', 13783, 926, 6, 4, 13409, 22628)
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(datetime.date(2000, 2, 18), datetime.time(17, 37), 'Hyderabad', 437045, Decimal('6.56'), 69, 'Winter', '98BA714D', 11, 30, 'Fe-500', 13291, 1220, 5, 6, 11749, 21790)
(datetime.date(2000, 2, 19), datetime.time(17, 28), 'Pune', 690577, Decimal('11.48'), 63, 'Winter', 'BA8E193D', 21, 5, 'Fe-500', 9954, 682, 3, 2, 12133, 19667)
(datetime.date(2000, 2, 20), datetime.time(9, 31), 'Bengaluru', 544005, Decimal('8.26'), 68, 'Rainy', 'EDFA35B7', 10, 19, 'Fe-500', 4039, 811, 8, 3, 8333, 18616)
(datetime.date(2000, 2, 21), datetime.time(13, 55), 'Bengaluru', 482129, Decimal('6.08'), 74, 'Winter', 'B6BFF2B4', 19, 26, 'Fe-500', 7865, 1155, 12, 6, 14464, 17057)
(datetime.date(2000, 2, 22), datetime.time(10, 21), 'Delhi', 493657, Decimal('8.64'), 58, 'Winter', '7ABCDC2B', 11, 30, 'Fe-415', 7984, 724, 6, 6, 12305, 16700)
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(datetime.date(2000, 2, 24), datetime.time(17, 16), 'Hyderabad', 579907, Decimal('9.51'), 63, 'Winter', '5D15BA24', 12, 18, 'Fe-600', 5634, 662, 12, 6, 10614, 18190)
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(datetime.date(2000, 3, 1), datetime.time(16, 51), 'Bengaluru', 539880, Decimal('8.29'), 63, 'Winter', '4D9A9828', 17, 5, 'Fe-600D', 13516, 1137, 8, 6, 13914, 22806)
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(datetime.date(2000, 3, 7), datetime.time(14, 37), 'Hyderabad', 499047, Decimal('11.00'), 51, 'Rainy', '9459AC7D', 18, 13, 'Fe-600D', 9100, 1143, 13, 4, 9775, 24200)
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(datetime.date(2000, 3, 9), datetime.time(15, 18), 'Bengaluru', 460016, Decimal('7.11'), 58, 'Winter', '6E4252A8', 11, 12, 'Fe-550', 12493, 682, 9, 5, 14151, 16927)
(datetime.date(2000, 3, 10), datetime.time(12, 36), 'Bengaluru', 693082, Decimal('11.74'), 59, 'Winter', '46214A4B', 22, 29, 'Fe-415', 14168, 781, 8, 4, 12472, 21570)
(datetime.date(2000, 3, 11), datetime.time(10, 43), 'Delhi', 450421, Decimal('7.50'), 61, 'Winter', 'C098DBB3', 19, 10, 'Fe-500', 4751, 842, 3, 4, 11573, 15998)
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(datetime.date(2000, 3, 21), datetime.time(11, 3), 'Kolkata', 609665, Decimal('12.37'), 50, 'Winter', 'E11E8330', 8, 30, 'Fe-415D', 4966, 968, 5, 2, 7240, 21335)
(datetime.date(2000, 3, 22), datetime.time(12, 5), 'Bengaluru', 627122, Decimal('9.84'), 64, 'Winter', 'DDF965B6', 14, 6, 'Fe-600', 10598, 1295, 5, 2, 13374, 15190)
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(datetime.date(2000, 3, 24), datetime.time(13, 30), 'Pune', 601699, Decimal('9.46'), 63, 'Winter', '84A6066A', 18, 16, 'Fe-550', 5119, 1242, 7, 4, 8119, 15258)
(datetime.date(2000, 3, 25), datetime.time(14, 55), 'Hyderabad', 467670, Decimal('6.75'), 72, 'Winter', 'C3DBE609', 15, 27, 'Fe-415', 11166, 1012, 5, 5, 7233, 19898)
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(datetime.date(2000, 4, 6), datetime.time(14, 21), 'Bengaluru', 626196, Decimal('9.70'), 67, 'Rainy', '8AD2A396', 21, 27, 'Fe-600D', 12966, 799, 10, 6, 11994, 15536)
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(datetime.date(2000, 4, 8), datetime.time(10, 47), 'Bengaluru', 621179, Decimal('8.86'), 69, 'Winter', 'DFDED493', 13, 18, 'Fe-550D', 7230, 1460, 12, 3, 11741, 22055)
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(datetime.date(2000, 4, 23), datetime.time(10, 0), 'Bengaluru', 532482, Decimal('8.68'), 61, 'Winter', '90963879', 11, 14, 'Fe-550D', 9498, 1420, 12, 6, 12418, 24413)
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(datetime.date(2000, 4, 25), datetime.time(13, 38), 'Hyderabad', 546031, Decimal('7.22'), 73, 'Winter', '5C47F868', 15, 26, 'Fe-415D', 14178, 709, 6, 4, 12612, 22020)
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(datetime.date(2000, 5, 7), datetime.time(17, 44), 'Hyderabad', 520039, Decimal('10.96'), 50, 'Winter', 'E2A6C35C', 26, 22, 'Fe-500', 10599, 1195, 6, 2, 8099, 20624)
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(datetime.date(2000, 5, 10), datetime.time(15, 0), 'Bengaluru', 653252, Decimal('11.16'), 62, 'Winter', '994D9163', 24, 9, 'Fe-600D', 8982, 1445, 5, 4, 9051, 18917)
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(datetime.date(2000, 5, 12), datetime.time(14, 44), 'Bengaluru', 506083, Decimal('7.92'), 60, 'Winter', '4398CE49', 24, 30, 'Fe-600D', 14834, 1453, 11, 4, 10468, 23559)
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(datetime.date(2000, 5, 17), datetime.time(16, 42), 'Delhi', 561378, Decimal('6.97'), 72, 'Winter', '9410E9C7', 13, 21, 'Fe-500', 4879, 780, 5, 5, 12058, 21656)
(datetime.date(2000, 5, 18), datetime.time(16, 9), 'Bengaluru', 600381, Decimal('7.87'), 71, 'Sunny', '3B544879', 26, 25, 'Fe-550', 9029, 1384, 3, 3, 12183, 20224)
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(datetime.date(2000, 5, 20), datetime.time(13, 25), 'Hyderabad', 482347, Decimal('7.05'), 63, 'Winter', '49D1CDFD', 12, 6, 'Fe-600', 3157, 588, 14, 4, 13861, 17887)
(datetime.date(2000, 5, 21), datetime.time(15, 42), 'Bengaluru', 518945, Decimal('7.45'), 72, 'Sunny', '00EB9862', 26, 28, 'Fe-415D', 5282, 513, 11, 1, 10805, 21203)
(datetime.date(2000, 5, 22), datetime.time(11, 29), 'Bengaluru', 438265, Decimal('6.16'), 70, 'Winter', '46E39DAD', 16, 25, 'Fe-415', 9773, 1259, 6, 4, 9550, 20900)
(datetime.date(2000, 5, 23), datetime.time(9, 41), 'Bengaluru', 462430, Decimal('8.22'), 52, 'Winter', 'C426ECB7', 11, 14, 'Fe-550D', 13987, 948, 13, 5, 8635, 17100)
(datetime.date(2000, 5, 24), datetime.time(16, 27), 'Kolkata', 565207, Decimal('8.47'), 66, 'Winter', '8D68CD7F', 20, 30, 'Fe-600D', 13843, 1455, 11, 7, 14168, 18967)
(datetime.date(2000, 5, 25), datetime.time(9, 24), 'Pune', 561499, Decimal('7.77'), 67, 'Winter', '778C6D95', 17, 21, 'Fe-550D', 10686, 1302, 8, 4, 7220, 16207)
(datetime.date(2000, 5, 26), datetime.time(14, 41), 'Pune', 485844, Decimal('7.74'), 58, 'Winter', 'A3D62BB3', 10, 12, 'Fe-550', 12979, 905, 5, 1, 9411, 24329)
(datetime.date(2000, 5, 27), datetime.time(11, 53), 'Delhi', 592478, Decimal('9.86'), 56, 'Winter', '77803887', 18, 19, 'Fe-500D', 8606, 1372, 10, 5, 7824, 21367)
(datetime.date(2000, 5, 28), datetime.time(13, 39), 'Hyderabad', 534653, Decimal('8.21'), 60, 'Winter', '3B471115', 24, 4, 'Fe-600D', 11737, 1174, 2, 7, 12468, 20711)
(datetime.date(2000, 5, 29), datetime.time(16, 12), 'Pune', 513097, Decimal('8.94'), 64, 'Winter', '56A67A7B', 16, 27, 'Fe-500', 12702, 1356, 14, 4, 14000, 21000)
(datetime.date(2000, 5, 30), datetime.time(11, 55), 'Delhi', 452775, Decimal('6.00'), 74, 'Rainy', 'ED731D02', 9, 25, 'Fe-600', 13082, 1274, 8, 7, 8985, 17763)
(datetime.date(2000, 5, 31), datetime.time(15, 45), 'Hyderabad', 657395, Decimal('11.13'), 56, 'Sunny', '78C237E5', 20, 25, 'Fe-500', 6635, 1252, 5, 2, 11527, 17622)
(datetime.date(2000, 6, 1), datetime.time(16, 27), 'Bengaluru', 474048, Decimal('7.80'), 52, 'Winter', '10E62B83', 18, 17, 'Fe-550D', 6349, 1245, 15, 3, 13015, 16245)
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(datetime.date(2000, 6, 3), datetime.time(14, 56), 'Hyderabad', 434921, Decimal('5.95'), 73, 'Winter', 'BCE51F21', 24, 9, 'Fe-550', 3321, 1246, 9, 3, 13588, 18433)
(datetime.date(2000, 6, 4), datetime.time(11, 30), 'Bengaluru', 650317, Decimal('10.39'), 59, 'Winter', '8E240293', 24, 23, 'Fe-500D', 14525, 683, 14, 7, 12335, 21545)
(datetime.date(2000, 6, 5), datetime.time(11, 32), 'Pune', 489556, Decimal('7.17'), 62, 'Winter', 'D44AA1FB', 29, 6, 'Fe-600', 9809, 936, 10, 5, 11712, 21734)
(datetime.date(2000, 6, 6), datetime.time(10, 21), 'Hyderabad', 639559, Decimal('11.10'), 52, 'Winter', '05F98A99', 18, 24, 'Fe-500', 6066, 1354, 3, 5, 7360, 19258)
(datetime.date(2000, 6, 7), datetime.time(15, 45), 'Pune', 509892, Decimal('7.40'), 65, 'Winter', '3744AB46', 12, 7, 'Fe-500', 5268, 1393, 13, 5, 7118, 19633)
(datetime.date(2000, 6, 8), datetime.time(15, 10), 'Hyderabad', 675804, Decimal('10.49'), 62, 'Rainy', '9712B551', 18, 7, 'Fe-550D', 13955, 937, 13, 1, 10352, 18747)
(datetime.date(2000, 6, 9), datetime.time(16, 30), 'Delhi', 513204, Decimal('8.90'), 56, 'Rainy', '2CB8222D', 28, 10, 'Fe-500', 3730, 676, 9, 4, 10289, 23064)
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(datetime.date(2000, 6, 13), datetime.time(12, 3), 'Hyderabad', 517701, Decimal('6.62'), 73, 'Winter', '41D81315', 12, 4, 'Fe-415', 11746, 1094, 4, 6, 11949, 23244)
(datetime.date(2000, 6, 14), datetime.time(10, 5), 'Delhi', 513110, Decimal('6.94'), 66, 'Rainy', 'CE573A11', 16, 24, 'Fe-550D', 3229, 744, 6, 5, 10668, 15671)
(datetime.date(2000, 6, 15), datetime.time(13, 41), 'Bengaluru', 482425, Decimal('7.37'), 62, 'Winter', '9D89BE41', 21, 31, 'Fe-415D', 3516, 846, 8, 6, 7221, 19555)
(datetime.date(2000, 6, 16), datetime.time(13, 30), 'Bengaluru', 498905, Decimal('8.67'), 64, 'Winter', '2797F1DF', 23, 6, 'Fe-415', 11984, 1473, 6, 4, 9056, 15987)
(datetime.date(2000, 6, 17), datetime.time(9, 54), 'Hyderabad', 571336, Decimal('10.11'), 53, 'Winter', '7CA229BE', 18, 31, 'Fe-415', 8430, 590, 14, 5, 10602, 19011)
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(datetime.date(2000, 6, 19), datetime.time(14, 45), 'Hyderabad', 475061, Decimal('6.55'), 67, 'Winter', '1AF4A763', 26, 25, 'Fe-500D', 11508, 574, 8, 1, 9331, 23690)
(datetime.date(2000, 6, 20), datetime.time(10, 57), 'Bengaluru', 608090, Decimal('11.65'), 51, 'Sunny', '343A3479', 15, 19, 'Fe-550', 6393, 862, 7, 2, 9213, 15630)
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(datetime.date(2000, 6, 24), datetime.time(12, 9), 'Hyderabad', 610571, Decimal('10.22'), 56, 'Rainy', 'A3D62BB3', 17, 25, 'Fe-550', 9701, 1053, 9, 4, 10517, 24824)
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(datetime.date(2000, 6, 26), datetime.time(10, 39), 'Hyderabad', 558322, Decimal('7.94'), 66, 'Winter', 'D789C4F8', 14, 14, 'Fe-550', 3039, 847, 2, 5, 7658, 16466)
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(datetime.date(2008, 2, 7), datetime.time(14, 8), 'Hyderabad', 540164, Decimal('9.84'), 59, 'Winter', '21A554E4', 11, 18, 'Fe-500', 7394, 1053, 14, 6, 9099, 15059)
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(datetime.date(2008, 2, 9), datetime.time(9, 51), 'Pune', 556788, Decimal('8.74'), 63, 'Winter', '5D15BA24', 28, 19, 'Fe-600', 6039, 503, 5, 2, 9321, 20144)
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(datetime.date(2008, 2, 13), datetime.time(11, 4), 'Hyderabad', 569987, Decimal('8.93'), 63, 'Rainy', '1B9A6141', 20, 6, 'Fe-550', 5650, 1042, 3, 5, 13701, 20719)
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(datetime.date(2008, 3, 4), datetime.time(16, 53), 'Hyderabad', 470767, Decimal('7.08'), 61, 'Sunny', '016530C3', 16, 8, 'Fe-600', 6620, 1326, 10, 5, 12843, 23286)
(datetime.date(2008, 3, 5), datetime.time(13, 45), 'Delhi', 581267, Decimal('8.46'), 67, 'Winter', '2E6742DF', 15, 9, 'Fe-415', 10583, 1220, 6, 6, 14534, 15834)
(datetime.date(2008, 3, 6), datetime.time(16, 42), 'Hyderabad', 535727, Decimal('9.09'), 53, 'Winter', '79920577', 16, 16, 'Fe-500D', 12136, 976, 4, 3, 7905, 21950)
(datetime.date(2008, 3, 7), datetime.time(15, 14), 'Kolkata', 546556, Decimal('7.02'), 71, 'Sunny', 'BEE2591E', 30, 16, 'Fe-550D', 7500, 1019, 8, 7, 14135, 18716)
(datetime.date(2008, 3, 8), datetime.time(12, 34), 'Pune', 676168, Decimal('10.90'), 58, 'Rainy', 'F8809295', 10, 13, 'Fe-500D', 8332, 559, 9, 3, 10055, 15869)
(datetime.date(2008, 3, 9), datetime.time(15, 53), 'Pune', 657434, Decimal('9.22'), 67, 'Winter', '128E9324', 18, 29, 'Fe-415D', 14911, 1093, 12, 5, 8582, 23261)
(datetime.date(2008, 3, 10), datetime.time(14, 14), 'Bengaluru', 486344, Decimal('9.01'), 54, 'Winter', '89BEF205', 23, 18, 'Fe-415', 7026, 1126, 14, 2, 11387, 18698)
(datetime.date(2008, 3, 11), datetime.time(14, 7), 'Pune', 691491, Decimal('12.45'), 53, 'Rainy', '504EBAF3', 13, 5, 'Fe-500D', 7143, 842, 5, 2, 14158, 16848)
(datetime.date(2008, 3, 12), datetime.time(14, 45), 'Bengaluru', 552780, Decimal('7.87'), 69, 'Rainy', 'B8969BAD', 23, 11, 'Fe-550', 5110, 1212, 11, 2, 11724, 17324)
(datetime.date(2008, 3, 13), datetime.time(17, 35), 'Delhi', 529698, Decimal('8.94'), 62, 'Rainy', '5B3FE5DD', 24, 31, 'Fe-415D', 3273, 559, 7, 4, 13671, 17435)
(datetime.date(2008, 3, 14), datetime.time(12, 17), 'Kolkata', 512735, Decimal('8.99'), 53, 'Rainy', 'E832FEA9', 9, 5, 'Fe-500D', 5170, 965, 5, 7, 14802, 17338)
(datetime.date(2008, 3, 15), datetime.time(10, 18), 'Bengaluru', 511816, Decimal('9.14'), 52, 'Sunny', '4398CE49', 16, 13, 'Fe-415', 8148, 1090, 7, 6, 10417, 20182)
(datetime.date(2008, 3, 16), datetime.time(14, 26), 'Bengaluru', 479633, Decimal('7.63'), 69, 'Rainy', '6E18B7B2', 19, 6, 'Fe-600', 11424, 623, 13, 6, 11952, 21756)
(datetime.date(2008, 3, 17), datetime.time(17, 25), 'Kolkata', 580708, Decimal('10.82'), 52, 'Winter', '66C6C754', 10, 7, 'Fe-415D', 14011, 791, 15, 3, 11034, 17877)
(datetime.date(2008, 3, 18), datetime.time(11, 50), 'Bengaluru', 612472, Decimal('10.19'), 62, 'Sunny', '805C30EC', 15, 27, 'Fe-500', 11714, 1208, 2, 2, 7198, 18526)
(datetime.date(2008, 3, 19), datetime.time(16, 20), 'Hyderabad', 468608, Decimal('8.40'), 60, 'Winter', 'DEAA2594', 13, 12, 'Fe-415', 11318, 702, 12, 2, 10278, 22950)
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(datetime.date(2008, 3, 22), datetime.time(17, 4), 'Bengaluru', 466816, Decimal('8.17'), 62, 'Sunny', '2242208E', 24, 15, 'Fe-415D', 10170, 1477, 11, 5, 7818, 20717)
(datetime.date(2008, 3, 23), datetime.time(10, 17), 'Delhi', 482082, Decimal('7.17'), 63, 'Winter', '18D5762C', 19, 7, 'Fe-500', 10244, 1250, 2, 3, 12006, 17752)
(datetime.date(2008, 3, 24), datetime.time(11, 19), 'Kolkata', 461847, Decimal('7.44'), 62, 'Winter', '2774368E', 9, 24, 'Fe-415', 13251, 1350, 9, 2, 9254, 19553)
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(datetime.date(2008, 3, 26), datetime.time(14, 44), 'Delhi', 663591, Decimal('9.90'), 60, 'Rainy', '134AA741', 18, 6, 'Fe-500D', 6954, 534, 14, 3, 8505, 21577)
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(datetime.date(2008, 3, 28), datetime.time(14, 38), 'Bengaluru', 489863, Decimal('8.69'), 56, 'Winter', '3744AB46', 12, 9, 'Fe-415D', 7470, 572, 5, 3, 10608, 23913)
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(datetime.date(2008, 3, 30), datetime.time(13, 7), 'Delhi', 695909, Decimal('11.98'), 62, 'Sunny', 'EAECE33D', 11, 17, 'Fe-500', 13664, 771, 15, 1, 7742, 17735)
(datetime.date(2008, 3, 31), datetime.time(13, 41), 'Hyderabad', 536281, Decimal('7.26'), 71, 'Winter', 'DB946EB7', 16, 21, 'Fe-500', 10432, 746, 9, 2, 10554, 23691)
(datetime.date(2008, 4, 1), datetime.time(10, 29), 'Hyderabad', 526514, Decimal('7.21'), 72, 'Sunny', '7010291D', 19, 25, 'Fe-415', 10957, 1297, 14, 4, 13693, 23593)
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(datetime.date(2008, 5, 2), datetime.time(15, 50), 'Pune', 606670, Decimal('9.00'), 71, 'Rainy', '6E8E0163', 15, 8, 'Fe-600', 3856, 636, 7, 1, 14980, 22918)
(datetime.date(2008, 5, 3), datetime.time(9, 38), 'Hyderabad', 453555, Decimal('6.41'), 66, 'Winter', 'BEB43EE5', 10, 22, 'Fe-415', 10457, 1010, 8, 4, 12775, 16590)
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(datetime.date(2008, 5, 7), datetime.time(17, 51), 'Kolkata', 689956, Decimal('10.47'), 69, 'Winter', '5DE77C93', 17, 15, 'Fe-600', 13221, 530, 9, 5, 14132, 20122)
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(datetime.date(2008, 5, 9), datetime.time(12, 51), 'Hyderabad', 444254, Decimal('8.43'), 52, 'Winter', '6E8E0163', 19, 27, 'Fe-415', 14806, 957, 10, 1, 9094, 23521)
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(datetime.date(2008, 5, 13), datetime.time(10, 41), 'Pune', 463992, Decimal('7.09'), 67, 'Winter', '886E3ADB', 12, 20, 'Fe-415D', 3859, 530, 13, 3, 12479, 24144)
(datetime.date(2008, 5, 14), datetime.time(10, 48), 'Kolkata', 437723, Decimal('7.69'), 60, 'Winter', '4921CD9C', 12, 18, 'Fe-415', 3815, 670, 3, 7, 12936, 19460)
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(datetime.date(2008, 5, 16), datetime.time(17, 33), 'Hyderabad', 539968, Decimal('8.58'), 59, 'Winter', 'BA4F763E', 11, 18, 'Fe-550', 11968, 1316, 5, 4, 14635, 15577)
(datetime.date(2008, 5, 17), datetime.time(11, 56), 'Bengaluru', 433310, Decimal('6.14'), 72, 'Winter', 'ED57B431', 29, 7, 'Fe-550', 9582, 1368, 5, 5, 14062, 16901)
(datetime.date(2008, 5, 18), datetime.time(17, 11), 'Hyderabad', 715872, Decimal('13.93'), 51, 'Sunny', 'D5AD62AD', 9, 10, 'Fe-500', 12507, 1479, 15, 5, 10376, 18020)
(datetime.date(2008, 5, 19), datetime.time(10, 17), 'Delhi', 603403, Decimal('9.94'), 51, 'Rainy', '1E2D0559', 9, 29, 'Fe-500', 6497, 810, 14, 6, 10877, 21757)
(datetime.date(2008, 5, 20), datetime.time(9, 26), 'Delhi', 538554, Decimal('8.84'), 61, 'Winter', '57D33344', 24, 19, 'Fe-600', 10103, 1202, 12, 2, 7682, 22636)
(datetime.date(2008, 5, 21), datetime.time(14, 48), 'Hyderabad', 432577, Decimal('6.27'), 71, 'Winter', 'A2DBA5A9', 16, 21, 'Fe-500D', 14582, 765, 11, 1, 9241, 22632)
(datetime.date(2008, 5, 22), datetime.time(17, 0), 'Kolkata', 435218, Decimal('6.69'), 67, 'Winter', '69BA8BC7', 9, 13, 'Fe-550', 4059, 570, 12, 1, 10295, 22422)
(datetime.date(2008, 5, 23), datetime.time(10, 57), 'Pune', 711298, Decimal('12.13'), 52, 'Sunny', '12F6692C', 10, 10, 'Fe-600', 8775, 1260, 6, 2, 10733, 16049)
(datetime.date(2008, 5, 24), datetime.time(17, 5), 'Hyderabad', 546892, Decimal('9.66'), 63, 'Rainy', '0410C8BB', 31, 15, 'Fe-600', 10863, 1295, 2, 5, 9930, 19718)
(datetime.date(2008, 5, 25), datetime.time(14, 48), 'Pune', 551559, Decimal('7.71'), 71, 'Rainy', '81F1306F', 19, 10, 'Fe-600D', 14325, 1154, 13, 3, 7209, 15527)
(datetime.date(2008, 5, 26), datetime.time(11, 1), 'Delhi', 484084, Decimal('9.02'), 52, 'Winter', 'F4E76691', 8, 11, 'Fe-600', 8682, 1200, 8, 4, 12883, 19028)
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(datetime.date(2008, 5, 30), datetime.time(13, 59), 'Bengaluru', 571989, Decimal('8.60'), 65, 'Winter', '224C95EB', 18, 8, 'Fe-600', 9018, 1446, 9, 3, 9894, 15891)
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(datetime.date(2008, 6, 2), datetime.time(15, 10), 'Bengaluru', 608798, Decimal('9.01'), 65, 'Rainy', '1FCE3F43', 11, 16, 'Fe-500D', 4926, 1458, 7, 1, 14098, 18802)
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(datetime.date(2008, 6, 4), datetime.time(10, 32), 'Kolkata', 525914, Decimal('8.80'), 57, 'Winter', 'DA62EEEA', 9, 15, 'Fe-415', 8537, 1348, 6, 6, 8041, 15670)
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(datetime.date(2008, 6, 6), datetime.time(15, 3), 'Pune', 572331, Decimal('9.01'), 54, 'Winter', 'C19FF7ED', 12, 8, 'Fe-550', 5587, 744, 15, 6, 7382, 21247)
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(datetime.date(2008, 6, 8), datetime.time(10, 31), 'Bengaluru', 683083, Decimal('10.30'), 64, 'Sunny', '2ECE88B4', 18, 6, 'Fe-415', 3004, 1214, 2, 4, 7057, 24025)
(datetime.date(2008, 6, 9), datetime.time(11, 24), 'Kolkata', 498096, Decimal('7.72'), 60, 'Winter', '9C32E335', 9, 22, 'Fe-415', 3370, 704, 8, 2, 12423, 15325)
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(datetime.date(2008, 6, 14), datetime.time(11, 3), 'Bengaluru', 466504, Decimal('7.30'), 68, 'Winter', '6E68C1B6', 14, 16, 'Fe-600', 10702, 829, 2, 6, 10771, 24539)
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(datetime.date(2008, 6, 16), datetime.time(11, 8), 'Pune', 520183, Decimal('8.59'), 58, 'Winter', '99719040', 13, 19, 'Fe-500D', 4607, 1484, 1, 4, 11845, 17316)
(datetime.date(2008, 6, 17), datetime.time(11, 59), 'Hyderabad', 466225, Decimal('7.63'), 57, 'Rainy', 'CC3A9383', 18, 16, 'Fe-415', 7894, 842, 5, 4, 11370, 24746)
(datetime.date(2008, 6, 18), datetime.time(16, 7), 'Pune', 531223, Decimal('7.55'), 67, 'Winter', '544ABC10', 9, 15, 'Fe-550D', 8022, 1097, 11, 7, 10195, 17653)
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(datetime.date(2009, 12, 13), datetime.time(10, 17), 'Hyderabad', 598607, Decimal('7.66'), 71, 'Rainy', 'ED57B431', 27, 15, 'Fe-600D', 13307, 1368, 1, 5, 7161, 19169)
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(datetime.date(2009, 12, 15), datetime.time(17, 23), 'Hyderabad', 614558, Decimal('13.30'), 50, 'Winter', 'F8082C33', 9, 7, 'Fe-415D', 6605, 1409, 6, 6, 14014, 19729)
(datetime.date(2009, 12, 16), datetime.time(11, 53), 'Hyderabad', 453534, Decimal('5.92'), 71, 'Winter', 'BCE55314', 14, 6, 'Fe-550D', 9821, 661, 7, 2, 8753, 22952)
(datetime.date(2009, 12, 17), datetime.time(15, 9), 'Kolkata', 511459, Decimal('7.74'), 67, 'Winter', '613BBB22', 17, 27, 'Fe-500D', 11161, 1486, 9, 2, 11765, 20198)
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(datetime.date(2009, 12, 19), datetime.time(9, 26), 'Bengaluru', 505674, Decimal('8.99'), 55, 'Winter', '28A3282B', 28, 28, 'Fe-500', 9263, 881, 10, 6, 12526, 24028)
(datetime.date(2009, 12, 20), datetime.time(14, 8), 'Delhi', 478101, Decimal('6.62'), 68, 'Winter', 'B26949B5', 11, 18, 'Fe-415', 4611, 1084, 6, 2, 7439, 21426)
(datetime.date(2009, 12, 21), datetime.time(12, 1), 'Bengaluru', 528186, Decimal('7.66'), 70, 'Winter', '2A9B6D5A', 13, 31, 'Fe-500', 9223, 945, 5, 5, 14583, 20941)
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(datetime.date(2010, 2, 20), datetime.time(17, 49), 'Pune', 635775, Decimal('11.48'), 60, 'Sunny', 'AE2EA6D9', 22, 15, 'Fe-500D', 13330, 841, 10, 7, 9655, 16564)
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(datetime.date(2010, 2, 24), datetime.time(13, 54), 'Pune', 529506, Decimal('7.10'), 71, 'Rainy', 'B15AAC11', 20, 15, 'Fe-550', 11150, 600, 4, 5, 7236, 16267)
(datetime.date(2010, 2, 25), datetime.time(10, 35), 'Hyderabad', 534088, Decimal('7.61'), 71, 'Rainy', 'D90630D7', 11, 6, 'Fe-500', 12645, 1318, 10, 6, 8874, 20375)
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(datetime.date(2010, 3, 3), datetime.time(11, 5), 'Bengaluru', 461720, Decimal('6.90'), 68, 'Rainy', '7A184274', 27, 11, 'Fe-600', 10102, 1428, 12, 5, 14491, 16909)
(datetime.date(2010, 3, 4), datetime.time(13, 18), 'Hyderabad', 553904, Decimal('9.35'), 65, 'Rainy', '7BB37B42', 14, 5, 'Fe-415', 4136, 1354, 6, 1, 10811, 22028)
(datetime.date(2010, 3, 5), datetime.time(14, 59), 'Pune', 560859, Decimal('8.93'), 61, 'Winter', 'D9DA2C54', 9, 22, 'Fe-550', 12782, 1446, 1, 6, 12370, 15401)
(datetime.date(2010, 3, 6), datetime.time(17, 0), 'Hyderabad', 601656, Decimal('10.57'), 60, 'Sunny', '9A9669F8', 23, 10, 'Fe-415', 5443, 1290, 2, 2, 14763, 22507)
(datetime.date(2010, 3, 7), datetime.time(10, 10), 'Pune', 574869, Decimal('9.20'), 58, 'Rainy', '52DEDD4E', 17, 20, 'Fe-500', 12585, 1415, 10, 4, 9192, 24467)
(datetime.date(2010, 3, 8), datetime.time(10, 50), 'Hyderabad', 510421, Decimal('8.48'), 67, 'Rainy', 'D113E807', 22, 31, 'Fe-415D', 3140, 555, 5, 5, 13482, 16589)
(datetime.date(2010, 3, 9), datetime.time(14, 5), 'Hyderabad', 589335, Decimal('9.42'), 62, 'Rainy', '22D19A72', 20, 12, 'Fe-415D', 11820, 619, 8, 7, 9445, 22503)
(datetime.date(2010, 3, 10), datetime.time(13, 55), 'Delhi', 553067, Decimal('6.68'), 73, 'Winter', '99C16FC0', 18, 8, 'Fe-500D', 10952, 1002, 9, 3, 10722, 17666)
(datetime.date(2010, 3, 11), datetime.time(16, 18), 'Kolkata', 572646, Decimal('10.05'), 52, 'Winter', '318FE868', 15, 15, 'Fe-550', 14449, 836, 9, 5, 7795, 16113)
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(datetime.date(2010, 3, 21), datetime.time(12, 30), 'Kolkata', 503574, Decimal('8.12'), 64, 'Winter', 'A635ED8C', 14, 20, 'Fe-415', 4629, 610, 11, 4, 12959, 23627)
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(datetime.date(2010, 3, 26), datetime.time(10, 5), 'Bengaluru', 602781, Decimal('11.51'), 51, 'Rainy', 'D15AB778', 15, 13, 'Fe-415D', 12648, 723, 11, 4, 10973, 22493)
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(datetime.date(2010, 3, 28), datetime.time(9, 12), 'Pune', 553319, Decimal('10.88'), 50, 'Rainy', 'E45352F5', 16, 9, 'Fe-500', 12936, 928, 4, 2, 14543, 15258)
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(datetime.date(2010, 3, 30), datetime.time(12, 36), 'Hyderabad', 440813, Decimal('6.51'), 65, 'Winter', '778C6D95', 11, 21, 'Fe-500D', 3004, 619, 8, 3, 12930, 22586)
(datetime.date(2010, 3, 31), datetime.time(14, 27), 'Hyderabad', 460653, Decimal('7.90'), 59, 'Winter', '33F59135', 17, 32, 'Fe-500', 5687, 1078, 11, 6, 13891, 15947)
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(datetime.date(2010, 4, 2), datetime.time(16, 34), 'Bengaluru', 584924, Decimal('10.29'), 60, 'Winter', 'D9ACA4EE', 20, 13, 'Fe-415D', 8874, 1256, 8, 5, 7390, 23442)
(datetime.date(2010, 4, 3), datetime.time(16, 33), 'Bengaluru', 642551, Decimal('10.91'), 54, 'Winter', '7A1005B4', 15, 13, 'Fe-550', 12178, 575, 5, 5, 13409, 21887)
(datetime.date(2010, 4, 4), datetime.time(11, 32), 'Delhi', 464664, Decimal('7.44'), 61, 'Rainy', '016530C3', 12, 17, 'Fe-600', 4263, 719, 8, 3, 10636, 16890)
(datetime.date(2010, 4, 5), datetime.time(15, 11), 'Bengaluru', 502251, Decimal('8.03'), 60, 'Winter', '38EBC7C9', 15, 20, 'Fe-600D', 12343, 1233, 1, 6, 9438, 24712)
(datetime.date(2010, 4, 6), datetime.time(12, 14), 'Hyderabad', 532621, Decimal('7.76'), 67, 'Sunny', 'F62A0865', 22, 22, 'Fe-600', 4608, 1352, 13, 6, 8705, 20454)
(datetime.date(2010, 4, 7), datetime.time(17, 38), 'Bengaluru', 438592, Decimal('6.80'), 63, 'Winter', '12AAE14D', 10, 20, 'Fe-600', 10220, 1483, 4, 2, 13128, 17218)
(datetime.date(2010, 4, 8), datetime.time(9, 51), 'Kolkata', 440863, Decimal('6.70'), 70, 'Winter', 'E2360696', 17, 17, 'Fe-415D', 13444, 782, 14, 4, 10943, 19321)
(datetime.date(2010, 4, 9), datetime.time(13, 44), 'Bengaluru', 499601, Decimal('8.60'), 55, 'Rainy', 'C221B4AD', 22, 30, 'Fe-500D', 6255, 1404, 12, 3, 12838, 23401)
(datetime.date(2010, 4, 10), datetime.time(14, 45), 'Bengaluru', 529925, Decimal('6.67'), 74, 'Rainy', 'A35B3847', 12, 5, 'Fe-500', 8509, 557, 10, 6, 8304, 19594)
(datetime.date(2010, 4, 11), datetime.time(15, 52), 'Bengaluru', 573874, Decimal('8.61'), 67, 'Rainy', 'C487BC54', 16, 32, 'Fe-600D', 6066, 825, 1, 4, 7314, 16657)
(datetime.date(2010, 4, 12), datetime.time(11, 32), 'Bengaluru', 469775, Decimal('6.22'), 73, 'Rainy', 'A8973479', 19, 23, 'Fe-415D', 8175, 836, 15, 4, 13466, 16623)
(datetime.date(2010, 4, 13), datetime.time(17, 59), 'Hyderabad', 626202, Decimal('12.06'), 51, 'Rainy', '677B62F4', 26, 26, 'Fe-600D', 4403, 1085, 6, 6, 7802, 16120)
(datetime.date(2010, 4, 14), datetime.time(17, 35), 'Bengaluru', 688862, Decimal('10.80'), 65, 'Sunny', '286691B3', 9, 17, 'Fe-550', 9828, 754, 3, 2, 11872, 22294)
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(datetime.date(2010, 4, 17), datetime.time(9, 21), 'Delhi', 653429, Decimal('8.56'), 73, 'Rainy', '72F5D6F3', 19, 20, 'Fe-550', 9734, 671, 9, 5, 7046, 17252)
(datetime.date(2010, 4, 18), datetime.time(16, 19), 'Kolkata', 442010, Decimal('8.35'), 55, 'Sunny', '281D4F18', 15, 24, 'Fe-415', 9035, 837, 14, 6, 11879, 23558)
(datetime.date(2010, 4, 19), datetime.time(10, 50), 'Bengaluru', 500929, Decimal('6.64'), 72, 'Rainy', '069A44C5', 30, 5, 'Fe-500', 6558, 522, 2, 3, 8265, 17160)
(datetime.date(2010, 4, 20), datetime.time(15, 15), 'Kolkata', 603184, Decimal('10.81'), 59, 'Rainy', 'BC411196', 30, 15, 'Fe-415D', 10023, 1009, 14, 5, 8966, 17315)
(datetime.date(2010, 4, 21), datetime.time(13, 42), 'Delhi', 557001, Decimal('7.82'), 66, 'Winter', 'A1AE069C', 20, 10, 'Fe-550', 10299, 1025, 11, 5, 13060, 19849)
(datetime.date(2010, 4, 22), datetime.time(13, 33), 'Kolkata', 558670, Decimal('8.97'), 70, 'Sunny', '8DA0A29C', 20, 10, 'Fe-415D', 9920, 691, 10, 5, 14398, 24155)
(datetime.date(2010, 4, 23), datetime.time(16, 26), 'Kolkata', 631524, Decimal('8.00'), 74, 'Sunny', '9747E5AC', 18, 23, 'Fe-550D', 8141, 1439, 12, 6, 12139, 22169)
(datetime.date(2010, 4, 24), datetime.time(13, 7), 'Hyderabad', 546165, Decimal('7.72'), 67, 'Rainy', '798D3490', 11, 30, 'Fe-500', 6738, 1117, 6, 3, 9014, 21789)
(datetime.date(2010, 4, 25), datetime.time(14, 35), 'Bengaluru', 574287, Decimal('7.61'), 65, 'Rainy', '17ECB3FE', 16, 29, 'Fe-500', 9041, 1141, 4, 4, 13865, 19002)
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(datetime.date(2010, 4, 27), datetime.time(12, 56), 'Hyderabad', 684079, Decimal('11.14'), 63, 'Rainy', 'DDF965B6', 10, 22, 'Fe-500', 7170, 1360, 13, 2, 10515, 18152)
(datetime.date(2010, 4, 28), datetime.time(10, 32), 'Hyderabad', 483296, Decimal('8.57'), 62, 'Winter', '4D35CC3A', 11, 29, 'Fe-600D', 10629, 583, 6, 5, 10621, 16550)
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(datetime.date(2010, 5, 1), datetime.time(10, 59), 'Bengaluru', 449317, Decimal('7.87'), 56, 'Winter', '41997911', 30, 16, 'Fe-415', 5803, 1332, 9, 5, 7090, 19214)
(datetime.date(2010, 5, 2), datetime.time(16, 29), 'Hyderabad', 555814, Decimal('7.91'), 69, 'Winter', '13C75E49', 10, 5, 'Fe-550', 8643, 1058, 3, 3, 8588, 24930)
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(datetime.date(2010, 5, 5), datetime.time(11, 47), 'Pune', 613900, Decimal('10.57'), 56, 'Winter', 'E296988B', 18, 30, 'Fe-500', 14007, 897, 13, 6, 11937, 22042)
(datetime.date(2010, 5, 6), datetime.time(14, 56), 'Hyderabad', 617289, Decimal('8.97'), 55, 'Rainy', '4B121B11', 15, 8, 'Fe-500D', 10707, 1441, 2, 5, 13151, 22574)
(datetime.date(2010, 5, 7), datetime.time(16, 4), 'Hyderabad', 523697, Decimal('7.76'), 69, 'Winter', '3C751F49', 25, 16, 'Fe-500D', 3249, 1163, 1, 6, 11917, 15106)
(datetime.date(2010, 5, 8), datetime.time(12, 2), 'Delhi', 540325, Decimal('8.27'), 71, 'Rainy', 'D79A69E1', 20, 21, 'Fe-550D', 5990, 828, 11, 7, 8591, 17977)
(datetime.date(2010, 5, 9), datetime.time(15, 15), 'Bengaluru', 519398, Decimal('8.68'), 68, 'Winter', '2774368E', 30, 19, 'Fe-415', 14408, 1325, 7, 1, 9956, 19680)
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(datetime.date(2010, 5, 12), datetime.time(10, 35), 'Hyderabad', 587093, Decimal('10.15'), 54, 'Sunny', '886A7257', 13, 18, 'Fe-550', 13786, 1008, 7, 4, 13718, 24052)
(datetime.date(2010, 5, 13), datetime.time(16, 27), 'Bengaluru', 667345, Decimal('10.84'), 65, 'Sunny', '70A85E98', 26, 5, 'Fe-600', 8801, 888, 6, 3, 8093, 22441)
(datetime.date(2010, 5, 14), datetime.time(12, 6), 'Hyderabad', 427207, Decimal('5.95'), 65, 'Winter', '99EDAF59', 23, 19, 'Fe-550D', 5398, 932, 12, 6, 9627, 22528)
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(datetime.date(2010, 5, 16), datetime.time(10, 0), 'Kolkata', 527606, Decimal('8.53'), 59, 'Winter', 'EEB6ECAF', 19, 22, 'Fe-550', 4318, 920, 12, 3, 13612, 24373)
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(datetime.date(2010, 5, 19), datetime.time(11, 21), 'Bengaluru', 576736, Decimal('10.27'), 55, 'Winter', 'B1881D2E', 29, 21, 'Fe-500D', 5452, 1021, 13, 6, 11485, 24244)
(datetime.date(2010, 5, 20), datetime.time(11, 30), 'Kolkata', 566116, Decimal('9.71'), 57, 'Winter', '016530C3', 13, 19, 'Fe-415D', 13028, 931, 13, 5, 7031, 16593)
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(datetime.date(2010, 5, 24), datetime.time(12, 57), 'Bengaluru', 552725, Decimal('8.79'), 65, 'Winter', 'B8E9853F', 19, 7, 'Fe-500', 6623, 1215, 4, 5, 10732, 17246)
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(datetime.date(2010, 5, 27), datetime.time(13, 35), 'Bengaluru', 581005, Decimal('6.58'), 73, 'Winter', '76CDE617', 25, 8, 'Fe-550D', 14728, 632, 4, 4, 10860, 23434)
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(datetime.date(2010, 5, 29), datetime.time(17, 5), 'Delhi', 486683, Decimal('7.22'), 70, 'Winter', '5A7E5F53', 26, 20, 'Fe-415', 11121, 675, 14, 6, 12888, 21294)
(datetime.date(2010, 5, 30), datetime.time(10, 53), 'Hyderabad', 552491, Decimal('9.53'), 55, 'Rainy', 'AE2EA6D9', 27, 17, 'Fe-550', 8307, 1381, 7, 2, 11943, 20010)
(datetime.date(2010, 5, 31), datetime.time(10, 47), 'Hyderabad', 603051, Decimal('8.20'), 71, 'Winter', '99719040', 24, 11, 'Fe-600', 12793, 1441, 8, 5, 10545, 20954)
(datetime.date(2010, 6, 1), datetime.time(14, 23), 'Bengaluru', 458102, Decimal('8.01'), 51, 'Winter', '21D71789', 10, 18, 'Fe-500D', 7275, 1461, 4, 5, 11146, 21820)
(datetime.date(2010, 6, 2), datetime.time(13, 32), 'Hyderabad', 586314, Decimal('9.51'), 59, 'Winter', 'AF83961C', 27, 10, 'Fe-415', 5207, 829, 14, 7, 8278, 22928)
(datetime.date(2010, 6, 3), datetime.time(16, 15), 'Hyderabad', 577141, Decimal('8.89'), 60, 'Winter', '2508D107', 19, 10, 'Fe-600', 5815, 1311, 12, 3, 14099, 22438)
(datetime.date(2010, 6, 4), datetime.time(13, 21), 'Delhi', 504396, Decimal('10.32'), 51, 'Winter', '97CDE880', 10, 18, 'Fe-415', 4008, 942, 14, 2, 11731, 21917)
(datetime.date(2010, 6, 5), datetime.time(16, 37), 'Kolkata', 506172, Decimal('8.07'), 67, 'Winter', '12312384', 20, 18, 'Fe-415', 7784, 1026, 6, 4, 8709, 23567)
(datetime.date(2010, 6, 6), datetime.time(16, 54), 'Pune', 492403, Decimal('9.29'), 50, 'Rainy', 'C993D9D8', 9, 7, 'Fe-600D', 10178, 1329, 4, 6, 12465, 15068)
(datetime.date(2010, 6, 7), datetime.time(17, 37), 'Bengaluru', 542812, Decimal('9.31'), 55, 'Sunny', 'B9374793', 9, 10, 'Fe-600', 12912, 885, 11, 6, 7835, 22184)
(datetime.date(2010, 6, 8), datetime.time(14, 23), 'Bengaluru', 504742, Decimal('8.80'), 57, 'Winter', 'C14376C6', 24, 24, 'Fe-550', 4820, 999, 8, 1, 9371, 21149)
(datetime.date(2010, 6, 9), datetime.time(14, 3), 'Kolkata', 620354, Decimal('8.90'), 71, 'Rainy', 'B39BBAA4', 11, 17, 'Fe-600D', 8431, 509, 8, 7, 9837, 20827)
(datetime.date(2010, 6, 10), datetime.time(14, 51), 'Hyderabad', 552656, Decimal('9.32'), 59, 'Rainy', 'CD4BC988', 11, 26, 'Fe-600', 12220, 970, 7, 6, 12691, 19412)
(datetime.date(2010, 6, 11), datetime.time(9, 13), 'Pune', 553737, Decimal('9.16'), 60, 'Winter', '46214A4B', 26, 10, 'Fe-600', 13972, 830, 9, 4, 10982, 24889)
(datetime.date(2010, 6, 12), datetime.time(16, 37), 'Hyderabad', 542552, Decimal('7.80'), 70, 'Rainy', 'D90630D7', 13, 13, 'Fe-550', 13093, 531, 4, 1, 12639, 21314)
(datetime.date(2010, 6, 13), datetime.time(14, 54), 'Delhi', 559099, Decimal('7.09'), 73, 'Rainy', '5C47F868', 29, 21, 'Fe-415', 11839, 940, 7, 5, 11665, 16743)
(datetime.date(2010, 6, 14), datetime.time(12, 16), 'Pune', 440679, Decimal('6.22'), 69, 'Winter', 'B778CEC3', 9, 30, 'Fe-500', 9933, 754, 7, 5, 13274, 15030)
(datetime.date(2010, 6, 15), datetime.time(9, 19), 'Kolkata', 458569, Decimal('8.03'), 54, 'Winter', 'F8A17F65', 22, 6, 'Fe-600', 6325, 1140, 12, 7, 9118, 22728)
(datetime.date(2010, 6, 16), datetime.time(16, 57), 'Kolkata', 554033, Decimal('10.51'), 52, 'Winter', '3D8B2957', 22, 32, 'Fe-600D', 10339, 643, 5, 3, 14417, 21537)
(datetime.date(2010, 6, 17), datetime.time(14, 46), 'Kolkata', 432514, Decimal('6.24'), 67, 'Winter', 'C2DB4579', 11, 31, 'Fe-500D', 8532, 921, 10, 4, 13124, 23389)
(datetime.date(2010, 6, 18), datetime.time(13, 45), 'Hyderabad', 681814, Decimal('10.80'), 61, 'Sunny', '01E88151', 18, 20, 'Fe-500', 12198, 917, 3, 3, 14096, 20244)
(datetime.date(2010, 6, 19), datetime.time(13, 59), 'Pune', 484991, Decimal('7.26'), 72, 'Rainy', '0CDAC243', 15, 30, 'Fe-415D', 5251, 596, 12, 1, 14234, 21079)
(datetime.date(2010, 6, 20), datetime.time(10, 58), 'Kolkata', 644372, Decimal('10.23'), 61, 'Rainy', 'FD81DFE8', 13, 13, 'Fe-600', 5788, 549, 10, 6, 8322, 22212)
(datetime.date(2010, 6, 21), datetime.time(9, 47), 'Delhi', 512525, Decimal('6.80'), 69, 'Rainy', '7EE2ACB2', 11, 31, 'Fe-500D', 11052, 1413, 2, 7, 12516, 18820)
(datetime.date(2010, 6, 22), datetime.time(16, 35), 'Hyderabad', 514895, Decimal('7.47'), 72, 'Winter', '6C866A0D', 17, 7, 'Fe-415D', 8011, 1267, 3, 6, 10589, 17631)
(datetime.date(2010, 6, 23), datetime.time(14, 26), 'Hyderabad', 637037, Decimal('9.92'), 54, 'Sunny', '2632760', 8, 6, 'Fe-500', 11862, 1356, 11, 2, 12792, 15198)
(datetime.date(2010, 6, 24), datetime.time(16, 9), 'Bengaluru', 475276, Decimal('6.53'), 67, 'Winter', 'C05964B7', 26, 29, 'Fe-600', 9123, 683, 9, 6, 11068, 18924)
(datetime.date(2010, 6, 25), datetime.time(11, 31), 'Hyderabad', 561704, Decimal('9.99'), 54, 'Sunny', '273589D2', 25, 9, 'Fe-500', 6203, 1060, 8, 3, 10820, 24986)
(datetime.date(2010, 6, 26), datetime.time(15, 18), 'Hyderabad', 589947, Decimal('9.24'), 69, 'Rainy', '9A9669F8', 19, 19, 'Fe-600D', 4752, 777, 10, 1, 9948, 23799)
(datetime.date(2010, 6, 27), datetime.time(14, 22), 'Delhi', 427193, Decimal('6.06'), 67, 'Winter', '35821E8D', 14, 12, 'Fe-600D', 11313, 1391, 4, 6, 10838, 18188)
(datetime.date(2010, 6, 28), datetime.time(11, 1), 'Kolkata', 637948, Decimal('9.41'), 65, 'Sunny', '238677DE', 14, 14, 'Fe-500', 7644, 586, 15, 3, 7185, 19416)
(datetime.date(2010, 6, 29), datetime.time(16, 20), 'Delhi', 707177, Decimal('12.96'), 57, 'Sunny', 'C4BA95A0', 31, 19, 'Fe-415D', 11024, 522, 11, 5, 14323, 19020)
(datetime.date(2010, 6, 30), datetime.time(13, 16), 'Bengaluru', 440699, Decimal('6.72'), 63, 'Winter', '432DC322', 24, 8, 'Fe-600', 14132, 1494, 10, 4, 8569, 22490)
(datetime.date(2010, 7, 1), datetime.time(13, 59), 'Kolkata', 695159, Decimal('12.57'), 53, 'Winter', 'E97FA07A', 25, 20, 'Fe-415', 13942, 674, 8, 5, 11015, 21908)
(datetime.date(2010, 7, 2), datetime.time(16, 18), 'Pune', 643086, Decimal('13.24'), 50, 'Sunny', 'F4E76691', 27, 9, 'Fe-600D', 14147, 752, 2, 4, 7396, 16748)
(datetime.date(2010, 7, 3), datetime.time(17, 46), 'Hyderabad', 567078, Decimal('9.33'), 60, 'Sunny', '7E594988', 24, 5, 'Fe-550D', 6897, 971, 8, 2, 11089, 20763)
(datetime.date(2010, 7, 4), datetime.time(9, 51), 'Pune', 658967, Decimal('8.83'), 60, 'Winter', '85B6C2EA', 24, 29, 'Fe-550D', 10325, 1115, 2, 4, 7299, 22650)
(datetime.date(2010, 7, 5), datetime.time(10, 42), 'Bengaluru', 536170, Decimal('9.43'), 53, 'Winter', 'CEDA8A3C', 26, 25, 'Fe-600', 14159, 1244, 11, 7, 7910, 21032)
(datetime.date(2010, 7, 6), datetime.time(11, 58), 'Delhi', 651182, Decimal('9.70'), 60, 'Rainy', '12F6692C', 22, 21, 'Fe-415D', 11587, 1413, 10, 2, 12365, 21708)
(datetime.date(2010, 7, 7), datetime.time(13, 4), 'Hyderabad', 577470, Decimal('7.34'), 73, 'Rainy', '84A6066A', 23, 10, 'Fe-500D', 9550, 699, 11, 4, 11337, 22879)
(datetime.date(2010, 7, 8), datetime.time(11, 18), 'Pune', 697571, Decimal('12.35'), 53, 'Rainy', '3B471115', 26, 18, 'Fe-600D', 9890, 799, 8, 2, 7794, 24399)
(datetime.date(2010, 7, 9), datetime.time(16, 8), 'Hyderabad', 616807, Decimal('9.15'), 64, 'Sunny', 'DEAA2594', 9, 6, 'Fe-550', 8593, 832, 11, 5, 14390, 21929)
(datetime.date(2010, 7, 10), datetime.time(10, 26), 'Delhi', 673340, Decimal('9.02'), 71, 'Sunny', '7ABCDC2B', 12, 25, 'Fe-500D', 4485, 1477, 7, 3, 12179, 23276)
(datetime.date(2010, 7, 11), datetime.time(12, 19), 'Hyderabad', 417797, Decimal('6.39'), 66, 'Winter', '5856E41B', 20, 13, 'Fe-500D', 7596, 1366, 5, 4, 8322, 23879)
(datetime.date(2010, 7, 12), datetime.time(11, 35), 'Delhi', 630545, Decimal('7.56'), 72, 'Winter', 'F6FFA672', 32, 18, 'Fe-550D', 8622, 1442, 4, 2, 10442, 18565)
(datetime.date(2010, 7, 13), datetime.time(9, 48), 'Delhi', 618766, Decimal('9.57'), 61, 'Winter', '9489E6E1', 27, 20, 'Fe-600', 13963, 1387, 9, 1, 11488, 18843)
(datetime.date(2010, 7, 14), datetime.time(12, 46), 'Pune', 440795, Decimal('8.39'), 51, 'Winter', '0DD6B878', 10, 21, 'Fe-415D', 7226, 1460, 2, 6, 10216, 23229)
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(datetime.date(2010, 7, 16), datetime.time(16, 23), 'Pune', 670093, Decimal('10.07'), 68, 'Rainy', '8B2C2D56', 9, 13, 'Fe-550D', 9674, 1417, 5, 3, 11430, 23681)
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(datetime.date(2010, 10, 1), datetime.time(15, 7), 'Bengaluru', 492824, Decimal('8.93'), 60, 'Winter', '153663AD', 28, 23, 'Fe-415D', 9883, 866, 5, 6, 10633, 24565)
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(datetime.date(2011, 1, 1), datetime.time(15, 38), 'Pune', 416653, Decimal('5.87'), 73, 'Winter', 'A4B3B35D', 13, 9, 'Fe-550D', 6884, 839, 5, 2, 7308, 22684)
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(datetime.date(2011, 1, 20), datetime.time(15, 56), 'Hyderabad', 439064, Decimal('6.09'), 73, 'Winter', '6E68C1B6', 16, 16, 'Fe-415', 14682, 614, 8, 5, 11231, 19797)
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(datetime.date(2014, 1, 30), datetime.time(15, 20), 'Hyderabad', 589337, Decimal('8.99'), 60, 'Rainy', 'E74836FB', 18, 6, 'Fe-500D', 10140, 1236, 3, 6, 8105, 16100)
(datetime.date(2014, 1, 31), datetime.time(11, 45), 'Hyderabad', 512433, Decimal('9.57'), 52, 'Winter', 'C70D7A8F', 11, 7, 'Fe-415D', 4533, 898, 3, 5, 9470, 22210)
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(datetime.date(2014, 2, 3), datetime.time(17, 43), 'Hyderabad', 504311, Decimal('6.37'), 71, 'Rainy', 'E6FA2B7E', 9, 15, 'Fe-550', 6665, 556, 4, 5, 14490, 15397)
(datetime.date(2014, 2, 4), datetime.time(12, 37), 'Hyderabad', 598232, Decimal('8.59'), 70, 'Sunny', '7AB549BE', 14, 20, 'Fe-600', 7520, 834, 12, 3, 13436, 16692)
(datetime.date(2014, 2, 5), datetime.time(10, 21), 'Bengaluru', 663426, Decimal('11.16'), 55, 'Winter', 'F9A6BD94', 28, 32, 'Fe-415', 12738, 988, 15, 5, 12749, 24157)
(datetime.date(2014, 2, 6), datetime.time(10, 39), 'Bengaluru', 465244, Decimal('6.79'), 67, 'Winter', '589EEB01', 28, 17, 'Fe-500', 6808, 872, 13, 4, 11820, 19718)
(datetime.date(2014, 2, 7), datetime.time(11, 1), 'Hyderabad', 554249, Decimal('11.86'), 50, 'Rainy', '97CDE880', 16, 28, 'Fe-500D', 11392, 739, 11, 3, 8009, 17268)
(datetime.date(2014, 2, 8), datetime.time(15, 45), 'Delhi', 636474, Decimal('9.93'), 64, 'Sunny', '029A3BB6', 11, 5, 'Fe-500', 12619, 782, 8, 3, 11949, 22782)
(datetime.date(2014, 2, 9), datetime.time(10, 14), 'Pune', 709756, Decimal('12.68'), 56, 'Winter', 'CF4DE6F5', 9, 8, 'Fe-600D', 7727, 682, 14, 7, 12937, 16551)
(datetime.date(2014, 2, 10), datetime.time(14, 48), 'Bengaluru', 532671, Decimal('8.09'), 70, 'Winter', '7A2A51CC', 9, 6, 'Fe-415D', 11540, 801, 11, 4, 8362, 19226)
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(datetime.date(2014, 2, 17), datetime.time(10, 19), 'Kolkata', 624145, Decimal('12.76'), 50, 'Sunny', 'BC58539A', 10, 8, 'Fe-600D', 10148, 1139, 7, 6, 11014, 16455)
(datetime.date(2014, 2, 18), datetime.time(15, 15), 'Bengaluru', 463072, Decimal('6.15'), 73, 'Winter', '4D404DEC', 8, 9, 'Fe-550', 12646, 1147, 7, 2, 8848, 20993)
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(datetime.date(2014, 2, 20), datetime.time(12, 9), 'Bengaluru', 424766, Decimal('7.69'), 52, 'Winter', '2B03148A', 19, 17, 'Fe-600D', 6912, 961, 11, 5, 11376, 18344)
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(datetime.date(2014, 3, 6), datetime.time(13, 8), 'Hyderabad', 672996, Decimal('10.86'), 63, 'Rainy', 'CFD3C543', 23, 24, 'Fe-500', 8538, 1255, 10, 7, 13104, 20099)
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(datetime.date(2014, 4, 4), datetime.time(16, 30), 'Hyderabad', 424383, Decimal('7.23'), 54, 'Rainy', 'E4BE19B9', 23, 23, 'Fe-600', 3847, 1413, 11, 7, 10532, 18225)
(datetime.date(2014, 4, 5), datetime.time(9, 1), 'Pune', 550730, Decimal('9.59'), 62, 'Winter', 'D115739E', 12, 31, 'Fe-415D', 10525, 1471, 7, 6, 7456, 24271)
(datetime.date(2014, 4, 6), datetime.time(13, 31), 'Delhi', 682038, Decimal('8.69'), 67, 'Sunny', '51D64BD7', 12, 16, 'Fe-550D', 6804, 707, 13, 4, 10085, 21717)
(datetime.date(2014, 4, 7), datetime.time(13, 40), 'Pune', 526523, Decimal('8.71'), 61, 'Winter', 'BF76EBDD', 10, 20, 'Fe-500D', 3347, 1116, 4, 5, 10037, 17228)
(datetime.date(2014, 4, 8), datetime.time(10, 37), 'Bengaluru', 685182, Decimal('12.31'), 54, 'Winter', '3B544879', 12, 30, 'Fe-500D', 12329, 568, 13, 5, 14392, 20912)
(datetime.date(2014, 4, 9), datetime.time(12, 56), 'Kolkata', 591516, Decimal('10.10'), 58, 'Rainy', '134AA741', 15, 30, 'Fe-600', 11107, 600, 4, 2, 10822, 21011)
(datetime.date(2014, 4, 10), datetime.time(14, 21), 'Delhi', 611475, Decimal('9.85'), 59, 'Winter', 'AB4EA4C3', 14, 28, 'Fe-500D', 13834, 792, 8, 3, 10220, 20597)
(datetime.date(2014, 4, 11), datetime.time(16, 5), 'Kolkata', 508712, Decimal('7.38'), 68, 'Winter', '5B3FE5DD', 26, 9, 'Fe-500D', 6745, 1348, 9, 5, 11893, 15418)
(datetime.date(2014, 4, 12), datetime.time(16, 34), 'Delhi', 594345, Decimal('10.84'), 52, 'Sunny', 'CC3A9383', 15, 20, 'Fe-415', 10458, 1190, 6, 2, 12201, 16482)
(datetime.date(2014, 4, 13), datetime.time(13, 44), 'Bengaluru', 513906, Decimal('7.73'), 69, 'Winter', '12B0A924', 27, 14, 'Fe-600', 10040, 997, 10, 2, 8464, 20170)
(datetime.date(2014, 4, 14), datetime.time(15, 21), 'Hyderabad', 561258, Decimal('7.48'), 65, 'Rainy', '6DF3A9A5', 13, 16, 'Fe-600', 13616, 1196, 14, 4, 14121, 22836)
(datetime.date(2014, 4, 15), datetime.time(17, 44), 'Delhi', 556236, Decimal('8.32'), 61, 'Rainy', 'B9968527', 14, 19, 'Fe-550D', 5976, 616, 14, 5, 13393, 20094)
(datetime.date(2014, 4, 16), datetime.time(12, 36), 'Pune', 526389, Decimal('7.75'), 68, 'Sunny', 'AEB1F8D5', 17, 16, 'Fe-500D', 6921, 667, 3, 4, 9154, 22281)
(datetime.date(2014, 4, 17), datetime.time(9, 50), 'Bengaluru', 558076, Decimal('9.84'), 53, 'Rainy', 'E29DA4F9', 9, 4, 'Fe-500', 4812, 508, 8, 5, 7395, 16332)
(datetime.date(2014, 4, 18), datetime.time(16, 26), 'Delhi', 430459, Decimal('8.66'), 52, 'Rainy', 'C5D92F51', 14, 20, 'Fe-500', 9704, 723, 10, 2, 8448, 15009)
(datetime.date(2014, 4, 19), datetime.time(13, 8), 'Delhi', 515354, Decimal('7.52'), 71, 'Rainy', '798D3490', 19, 12, 'Fe-550D', 8334, 1228, 12, 5, 12845, 24749)
(datetime.date(2014, 4, 20), datetime.time(14, 24), 'Pune', 476125, Decimal('6.91'), 69, 'Winter', '69BA8BC7', 16, 22, 'Fe-415D', 6388, 999, 4, 5, 12678, 18671)
(datetime.date(2014, 4, 21), datetime.time(13, 33), 'Kolkata', 655337, Decimal('10.49'), 58, 'Winter', '13E58983', 16, 27, 'Fe-500D', 7296, 1116, 8, 6, 8359, 23896)
(datetime.date(2014, 4, 22), datetime.time(11, 29), 'Pune', 519360, Decimal('7.54'), 70, 'Winter', '51FB51F9', 19, 17, 'Fe-550', 4508, 892, 8, 1, 7972, 23576)
(datetime.date(2014, 4, 23), datetime.time(10, 12), 'Bengaluru', 557636, Decimal('8.28'), 68, 'Rainy', '68000E4C', 10, 8, 'Fe-550', 7708, 1431, 8, 5, 14578, 20779)
(datetime.date(2014, 4, 24), datetime.time(15, 11), 'Pune', 636263, Decimal('8.47'), 73, 'Winter', 'EE834345', 15, 30, 'Fe-415', 14249, 1223, 10, 3, 7659, 17171)
(datetime.date(2014, 4, 25), datetime.time(12, 43), 'Kolkata', 561409, Decimal('10.34'), 52, 'Winter', 'CCBB1BC9', 12, 26, 'Fe-500D', 11286, 874, 4, 3, 8423, 19643)
(datetime.date(2014, 4, 26), datetime.time(10, 21), 'Delhi', 636584, Decimal('10.65'), 67, 'Winter', '7B0842CE', 13, 7, 'Fe-500D', 8520, 1140, 1, 5, 14063, 18716)
(datetime.date(2014, 4, 27), datetime.time(10, 4), 'Hyderabad', 458439, Decimal('7.39'), 57, 'Rainy', 'D376FBA1', 14, 14, 'Fe-550', 3370, 1009, 3, 5, 8725, 24553)
(datetime.date(2014, 4, 28), datetime.time(9, 33), 'Hyderabad', 551484, Decimal('7.50'), 73, 'Winter', '748E2490', 9, 31, 'Fe-415', 11193, 707, 8, 2, 7588, 19040)
(datetime.date(2014, 4, 29), datetime.time(9, 33), 'Hyderabad', 556378, Decimal('7.98'), 68, 'Winter', 'B15AAC11', 12, 24, 'Fe-500D', 14181, 1475, 5, 6, 9742, 19930)
(datetime.date(2014, 4, 30), datetime.time(17, 8), 'Hyderabad', 519873, Decimal('6.91'), 70, 'Rainy', '7A3BB1D6', 28, 23, 'Fe-550', 5377, 507, 5, 5, 10709, 17082)
(datetime.date(2014, 5, 1), datetime.time(15, 33), 'Bengaluru', 611770, Decimal('12.10'), 55, 'Rainy', '12F6692C', 22, 6, 'Fe-600D', 12901, 711, 7, 2, 14334, 22710)
(datetime.date(2014, 5, 2), datetime.time(16, 52), 'Bengaluru', 502430, Decimal('8.24'), 68, 'Winter', '64C57765', 17, 15, 'Fe-500', 11467, 1496, 14, 2, 9766, 15347)
(datetime.date(2014, 5, 3), datetime.time(15, 22), 'Bengaluru', 527109, Decimal('8.12'), 59, 'Winter', 'AA92B5B8', 21, 14, 'Fe-550', 11367, 662, 14, 2, 9807, 17196)
(datetime.date(2014, 5, 4), datetime.time(10, 21), 'Pune', 584432, Decimal('8.52'), 65, 'Winter', '635148F9', 17, 29, 'Fe-500', 11704, 1216, 4, 2, 10315, 24721)
(datetime.date(2014, 5, 5), datetime.time(16, 5), 'Pune', 533521, Decimal('7.55'), 70, 'Winter', 'B49D28AA', 29, 10, 'Fe-415', 5971, 646, 10, 1, 8205, 20496)
(datetime.date(2014, 5, 6), datetime.time(12, 56), 'Kolkata', 458090, Decimal('7.79'), 57, 'Rainy', '2B25B13D', 17, 8, 'Fe-600D', 7533, 1326, 10, 6, 10035, 16645)
(datetime.date(2014, 5, 7), datetime.time(12, 2), 'Delhi', 594236, Decimal('8.15'), 61, 'Winter', '3488F938', 8, 29, 'Fe-550D', 3717, 505, 9, 3, 9015, 17348)
(datetime.date(2014, 5, 8), datetime.time(16, 6), 'Bengaluru', 592439, Decimal('7.84'), 71, 'Rainy', 'DDA24101', 9, 7, 'Fe-550D', 13388, 1413, 13, 7, 12885, 15933)
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(datetime.date(2014, 5, 10), datetime.time(15, 22), 'Pune', 507180, Decimal('9.34'), 54, 'Winter', 'C13394BC', 19, 18, 'Fe-600D', 9074, 764, 14, 6, 10750, 16176)
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(datetime.date(2014, 5, 13), datetime.time(16, 20), 'Kolkata', 490649, Decimal('8.00'), 62, 'Rainy', '75ED970C', 24, 11, 'Fe-600', 11092, 850, 4, 5, 12442, 22212)
(datetime.date(2014, 5, 14), datetime.time(15, 4), 'Pune', 655973, Decimal('8.93'), 73, 'Sunny', 'D7D56812', 8, 27, 'Fe-500D', 6120, 1049, 6, 5, 9628, 15346)
(datetime.date(2014, 5, 15), datetime.time(11, 50), 'Kolkata', 466099, Decimal('7.37'), 57, 'Winter', 'B11B34FE', 8, 16, 'Fe-500', 10839, 1441, 4, 2, 8472, 22464)
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(datetime.date(2014, 5, 21), datetime.time(15, 0), 'Hyderabad', 420152, Decimal('6.71'), 62, 'Winter', '9C32E335', 15, 19, 'Fe-500', 13841, 1107, 14, 7, 10112, 22557)
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(datetime.date(2014, 8, 5), datetime.time(10, 0), 'Hyderabad', 427929, Decimal('5.86'), 74, 'Winter', 'E45352F5', 22, 13, 'Fe-550', 13520, 649, 15, 2, 9275, 23926)
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(datetime.date(2014, 8, 8), datetime.time(9, 15), 'Hyderabad', 453585, Decimal('7.09'), 62, 'Winter', 'EBDEEF80', 27, 8, 'Fe-500', 12097, 619, 5, 3, 7407, 17060)
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(datetime.date(2014, 8, 29), datetime.time(15, 47), 'Hyderabad', 456591, Decimal('7.54'), 54, 'Winter', '6D73A888', 30, 9, 'Fe-550', 9111, 1224, 9, 3, 10735, 23548)
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(datetime.date(2014, 8, 31), datetime.time(11, 30), 'Pune', 622956, Decimal('10.81'), 53, 'Winter', '76BDB7AB', 17, 26, 'Fe-415D', 9207, 1269, 3, 3, 13147, 15481)
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(datetime.date(2014, 9, 2), datetime.time(11, 12), 'Bengaluru', 474252, Decimal('6.25'), 70, 'Winter', '4B121B11', 27, 31, 'Fe-500', 4856, 1084, 1, 2, 12825, 17155)
(datetime.date(2014, 9, 3), datetime.time(15, 20), 'Bengaluru', 533222, Decimal('7.62'), 66, 'Winter', '3CC14FEB', 18, 16, 'Fe-500', 5596, 598, 11, 7, 12923, 23326)
(datetime.date(2014, 9, 4), datetime.time(13, 48), 'Hyderabad', 506843, Decimal('6.24'), 72, 'Winter', '4B78330E', 14, 14, 'Fe-550D', 14965, 1018, 10, 4, 13315, 24360)
(datetime.date(2014, 9, 5), datetime.time(15, 4), 'Bengaluru', 642531, Decimal('9.98'), 62, 'Rainy', '5066B632', 21, 14, 'Fe-600D', 13758, 1298, 6, 4, 12314, 23898)
(datetime.date(2014, 9, 6), datetime.time(17, 35), 'Bengaluru', 487680, Decimal('7.09'), 70, 'Rainy', 'D789C4F8', 24, 5, 'Fe-550D', 14848, 942, 8, 6, 8526, 15707)
(datetime.date(2014, 9, 7), datetime.time(13, 1), 'Delhi', 525614, Decimal('10.16'), 53, 'Rainy', 'DB489B4C', 26, 23, 'Fe-550', 4478, 554, 10, 1, 11494, 17832)
(datetime.date(2014, 9, 8), datetime.time(11, 42), 'Delhi', 456710, Decimal('6.51'), 67, 'Rainy', '5581DAB2', 18, 14, 'Fe-600', 10950, 1202, 3, 7, 12535, 18908)
(datetime.date(2014, 9, 9), datetime.time(9, 41), 'Bengaluru', 629838, Decimal('9.66'), 65, 'Rainy', 'FEE146E7', 16, 28, 'Fe-500D', 13070, 824, 3, 6, 10573, 15615)
(datetime.date(2014, 9, 10), datetime.time(12, 58), 'Hyderabad', 434958, Decimal('7.09'), 64, 'Winter', '9712B551', 10, 8, 'Fe-415', 10031, 1387, 3, 7, 13580, 24009)
(datetime.date(2014, 9, 11), datetime.time(14, 20), 'Pune', 576975, Decimal('10.41'), 53, 'Winter', 'B9374793', 19, 28, 'Fe-415D', 14354, 946, 9, 5, 7492, 22093)
(datetime.date(2014, 9, 12), datetime.time(15, 18), 'Bengaluru', 668833, Decimal('10.90'), 67, 'Rainy', '5856E41B', 17, 28, 'Fe-500', 10271, 642, 3, 1, 10005, 19666)
(datetime.date(2014, 9, 13), datetime.time(14, 8), 'Bengaluru', 445979, Decimal('6.21'), 73, 'Winter', '69D985A5', 12, 13, 'Fe-415', 11797, 1298, 9, 7, 11431, 15066)
(datetime.date(2014, 9, 14), datetime.time(15, 41), 'Pune', 624310, Decimal('8.21'), 72, 'Winter', '753FA2ED', 28, 15, 'Fe-500', 12834, 957, 12, 3, 7368, 23625)
(datetime.date(2014, 9, 15), datetime.time(14, 15), 'Pune', 497978, Decimal('8.49'), 53, 'Winter', 'EBC32ADB', 11, 7, 'Fe-500', 11209, 927, 5, 7, 10369, 20239)
(datetime.date(2014, 9, 16), datetime.time(15, 32), 'Pune', 451569, Decimal('6.29'), 73, 'Winter', '635148F9', 24, 25, 'Fe-600D', 12011, 622, 12, 2, 14465, 18646)
(datetime.date(2014, 9, 17), datetime.time(15, 29), 'Bengaluru', 446888, Decimal('7.03'), 61, 'Winter', '942739F7', 17, 8, 'Fe-600', 12861, 812, 8, 2, 10806, 22178)
(datetime.date(2014, 9, 18), datetime.time(13, 41), 'Delhi', 600628, Decimal('9.64'), 55, 'Winter', 'E29DA4F9', 16, 6, 'Fe-600D', 14211, 1062, 1, 2, 9512, 15048)
(datetime.date(2014, 9, 19), datetime.time(12, 24), 'Bengaluru', 601493, Decimal('10.33'), 53, 'Rainy', '8E79ED83', 10, 18, 'Fe-550D', 11005, 790, 5, 4, 13185, 20177)
(datetime.date(2014, 9, 20), datetime.time(17, 44), 'Kolkata', 635857, Decimal('10.19'), 62, 'Winter', 'DCB940C4', 26, 6, 'Fe-415', 6934, 945, 5, 6, 8850, 20711)
(datetime.date(2014, 9, 21), datetime.time(9, 33), 'Pune', 544436, Decimal('7.65'), 66, 'Winter', '5CAEFDAF', 29, 12, 'Fe-550', 12509, 1304, 9, 4, 14798, 23079)
(datetime.date(2014, 9, 22), datetime.time(15, 40), 'Delhi', 651086, Decimal('10.24'), 56, 'Rainy', '2E446FD6', 8, 9, 'Fe-550', 6817, 832, 7, 1, 14394, 16650)
(datetime.date(2014, 9, 23), datetime.time(12, 53), 'Kolkata', 656126, Decimal('9.79'), 64, 'Sunny', '8E77EDC3', 15, 16, 'Fe-550', 12929, 1319, 10, 3, 12330, 23534)
(datetime.date(2014, 9, 24), datetime.time(17, 15), 'Kolkata', 507460, Decimal('7.43'), 67, 'Sunny', '60C1B95B', 11, 18, 'Fe-600D', 14349, 1274, 9, 3, 11296, 24217)
(datetime.date(2014, 9, 25), datetime.time(11, 29), 'Hyderabad', 622719, Decimal('10.90'), 55, 'Rainy', '3384A891', 29, 17, 'Fe-500D', 9435, 899, 3, 2, 11518, 21263)
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(datetime.date(2014, 9, 30), datetime.time(17, 8), 'Bengaluru', 499739, Decimal('7.37'), 64, 'Rainy', '8ECE482A', 15, 24, 'Fe-500D', 13819, 1238, 9, 2, 14204, 24777)
(datetime.date(2014, 10, 1), datetime.time(10, 20), 'Kolkata', 505954, Decimal('9.10'), 56, 'Winter', '46AAD8E3', 25, 27, 'Fe-500', 9601, 1037, 6, 1, 9597, 20198)
(datetime.date(2014, 10, 2), datetime.time(16, 29), 'Pune', 591918, Decimal('8.94'), 63, 'Winter', 'B80E8AC7', 12, 25, 'Fe-500D', 13702, 1074, 5, 6, 12055, 22863)
(datetime.date(2014, 10, 3), datetime.time(9, 31), 'Bengaluru', 575066, Decimal('8.61'), 65, 'Winter', '8AD2A396', 24, 13, 'Fe-500D', 14947, 1218, 10, 2, 12926, 24248)
(datetime.date(2014, 10, 4), datetime.time(16, 5), 'Hyderabad', 533759, Decimal('9.03'), 53, 'Winter', '2DE3998E', 11, 8, 'Fe-600D', 12311, 1098, 12, 5, 11013, 15794)
(datetime.date(2014, 10, 5), datetime.time(10, 28), 'Kolkata', 476809, Decimal('6.88'), 68, 'Rainy', 'D90630D7', 10, 19, 'Fe-500D', 7403, 1376, 5, 6, 14506, 24807)
(datetime.date(2014, 10, 6), datetime.time(14, 5), 'Delhi', 464646, Decimal('7.51'), 66, 'Winter', '33F59135', 10, 28, 'Fe-500', 13369, 691, 15, 3, 8472, 20797)
(datetime.date(2014, 10, 7), datetime.time(9, 48), 'Hyderabad', 496248, Decimal('9.10'), 56, 'Rainy', 'B778CEC3', 9, 7, 'Fe-415D', 9802, 1408, 7, 6, 11862, 15441)
(datetime.date(2014, 10, 8), datetime.time(14, 41), 'Hyderabad', 570675, Decimal('8.47'), 65, 'Rainy', 'C098DBB3', 18, 27, 'Fe-500', 12169, 1466, 9, 7, 11659, 17713)
(datetime.date(2014, 10, 9), datetime.time(16, 17), 'Bengaluru', 536537, Decimal('6.88'), 73, 'Winter', 'E74836FB', 18, 5, 'Fe-600D', 3215, 847, 2, 3, 10429, 19943)
(datetime.date(2014, 10, 10), datetime.time(16, 20), 'Bengaluru', 426323, Decimal('8.04'), 54, 'Winter', '22D19A72', 25, 11, 'Fe-415D', 14924, 1186, 12, 3, 12751, 19931)
(datetime.date(2014, 10, 11), datetime.time(15, 48), 'Kolkata', 445737, Decimal('5.91'), 74, 'Sunny', '5066B632', 18, 12, 'Fe-600D', 10209, 849, 15, 4, 7362, 17538)
(datetime.date(2014, 10, 12), datetime.time(11, 25), 'Hyderabad', 617521, Decimal('9.82'), 63, 'Sunny', '65B33CE5', 22, 25, 'Fe-600', 12905, 1247, 6, 2, 13941, 22386)
(datetime.date(2014, 10, 13), datetime.time(10, 26), 'Pune', 706926, Decimal('12.13'), 58, 'Winter', 'D335CD9E', 12, 20, 'Fe-600', 6547, 712, 7, 2, 10411, 21158)
(datetime.date(2014, 10, 14), datetime.time(10, 28), 'Kolkata', 541780, Decimal('9.82'), 56, 'Rainy', '9FA9E8B7', 22, 30, 'Fe-500D', 10609, 1190, 2, 2, 13823, 19383)
(datetime.date(2014, 10, 15), datetime.time(16, 29), 'Kolkata', 472910, Decimal('8.67'), 56, 'Rainy', '1E2D0559', 25, 23, 'Fe-500', 3881, 753, 6, 2, 14296, 20950)
(datetime.date(2014, 10, 16), datetime.time(12, 5), 'Hyderabad', 524792, Decimal('8.30'), 72, 'Sunny', '84366AD4', 18, 21, 'Fe-500D', 3342, 1076, 4, 1, 8922, 21106)
(datetime.date(2014, 10, 17), datetime.time(15, 19), 'Delhi', 492402, Decimal('10.34'), 50, 'Winter', '3.10E+196', 22, 7, 'Fe-600D', 13009, 968, 11, 6, 11744, 22243)
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(datetime.date(2014, 10, 19), datetime.time(9, 30), 'Bengaluru', 689002, Decimal('10.09'), 70, 'Sunny', 'D1047683', 20, 30, 'Fe-415D', 6655, 840, 6, 3, 9754, 15779)
(datetime.date(2014, 10, 20), datetime.time(9, 1), 'Bengaluru', 574150, Decimal('9.56'), 62, 'Winter', '30B53C02', 18, 22, 'Fe-550', 14328, 1313, 10, 5, 14592, 21705)
(datetime.date(2014, 10, 21), datetime.time(17, 17), 'Bengaluru', 597064, Decimal('9.73'), 61, 'Sunny', '82611D3E', 20, 27, 'Fe-500D', 5081, 989, 7, 5, 12187, 20258)
(datetime.date(2014, 10, 22), datetime.time(16, 1), 'Delhi', 419253, Decimal('6.14'), 66, 'Winter', '5EBC9DFC', 17, 13, 'Fe-600', 6323, 1138, 12, 3, 8587, 21677)
(datetime.date(2014, 10, 23), datetime.time(16, 9), 'Bengaluru', 657359, Decimal('11.11'), 55, 'Winter', 'C7B385A3', 9, 26, 'Fe-600D', 11402, 1178, 8, 6, 12019, 20384)
(datetime.date(2014, 10, 24), datetime.time(14, 8), 'Hyderabad', 474586, Decimal('8.30'), 57, 'Winter', 'D0F4A1C4', 16, 23, 'Fe-415D', 6615, 1247, 5, 2, 10035, 17419)
(datetime.date(2014, 10, 25), datetime.time(15, 22), 'Bengaluru', 551506, Decimal('8.03'), 65, 'Rainy', '5C47F868', 12, 23, 'Fe-500D', 11465, 1087, 13, 5, 12234, 24374)
(datetime.date(2014, 10, 26), datetime.time(14, 31), 'Delhi', 533030, Decimal('9.19'), 65, 'Winter', 'A6951325', 15, 20, 'Fe-500', 3245, 519, 9, 3, 9994, 18938)
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(datetime.date(2014, 10, 28), datetime.time(11, 31), 'Delhi', 550995, Decimal('8.69'), 60, 'Winter', '26D851A4', 20, 30, 'Fe-415D', 14282, 866, 12, 6, 14675, 18943)
(datetime.date(2014, 10, 29), datetime.time(17, 35), 'Pune', 468617, Decimal('8.24'), 53, 'Rainy', '4DCED490', 17, 7, 'Fe-550D', 10908, 1116, 9, 7, 14761, 15616)
(datetime.date(2014, 10, 30), datetime.time(11, 47), 'Pune', 466145, Decimal('7.17'), 62, 'Winter', 'C922D599', 12, 14, 'Fe-550', 9144, 1456, 4, 7, 12605, 24034)
(datetime.date(2014, 10, 31), datetime.time(14, 0), 'Bengaluru', 539100, Decimal('8.34'), 58, 'Rainy', '73AA2857', 29, 6, 'Fe-415', 14340, 1117, 7, 7, 12119, 19270)
(datetime.date(2014, 11, 1), datetime.time(12, 46), 'Delhi', 535306, Decimal('8.24'), 67, 'Winter', '18B11777', 15, 7, 'Fe-600', 10762, 650, 7, 3, 7244, 23711)
(datetime.date(2014, 11, 2), datetime.time(13, 18), 'Bengaluru', 576833, Decimal('9.03'), 64, 'Rainy', 'D1191AF8', 24, 23, 'Fe-415D', 7591, 1141, 7, 3, 10719, 17208)
(datetime.date(2014, 11, 3), datetime.time(10, 4), 'Bengaluru', 440885, Decimal('5.93'), 73, 'Winter', '85172B2D', 24, 22, 'Fe-415', 9556, 961, 2, 2, 7080, 24674)
(datetime.date(2014, 11, 4), datetime.time(9, 33), 'Delhi', 489228, Decimal('8.96'), 55, 'Winter', '1BCD1E42', 13, 19, 'Fe-415', 10797, 745, 12, 7, 9403, 17723)
(datetime.date(2014, 11, 5), datetime.time(11, 15), 'Bengaluru', 630082, Decimal('10.18'), 58, 'Rainy', 'EE75A871', 11, 10, 'Fe-500', 11339, 928, 10, 3, 10586, 17049)
(datetime.date(2014, 11, 6), datetime.time(15, 40), 'Hyderabad', 526009, Decimal('8.51'), 60, 'Winter', '9E9E87A1', 16, 21, 'Fe-600', 12820, 654, 11, 2, 13367, 21042)
(datetime.date(2014, 11, 7), datetime.time(13, 15), 'Pune', 571611, Decimal('9.50'), 57, 'Rainy', 'B8BE6082', 13, 20, 'Fe-500D', 3854, 900, 4, 5, 10484, 19414)
(datetime.date(2014, 11, 8), datetime.time(12, 2), 'Bengaluru', 634937, Decimal('10.41'), 54, 'Sunny', '04876EC9', 10, 27, 'Fe-600', 14921, 824, 13, 2, 11844, 17718)
(datetime.date(2014, 11, 9), datetime.time(10, 32), 'Kolkata', 628558, Decimal('9.32'), 70, 'Sunny', '66ECDDC2', 29, 14, 'Fe-600D', 8281, 1228, 1, 6, 11817, 20119)
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(datetime.date(2014, 11, 12), datetime.time(13, 35), 'Pune', 503942, Decimal('10.44'), 53, 'Sunny', '9552E58A', 16, 18, 'Fe-415', 10427, 1198, 7, 4, 9741, 22629)
(datetime.date(2014, 11, 13), datetime.time(10, 17), 'Hyderabad', 531377, Decimal('8.05'), 63, 'Winter', '7E594988', 15, 28, 'Fe-550', 11044, 726, 15, 2, 9091, 20644)
(datetime.date(2014, 11, 14), datetime.time(13, 48), 'Bengaluru', 513538, Decimal('8.36'), 55, 'Rainy', '33DE7E4D', 16, 22, 'Fe-550', 11522, 1481, 3, 5, 10690, 20480)
(datetime.date(2014, 11, 15), datetime.time(16, 5), 'Delhi', 699473, Decimal('10.89'), 70, 'Rainy', 'A4B3B35D', 9, 6, 'Fe-500', 8603, 608, 8, 4, 10666, 15455)
(datetime.date(2014, 11, 16), datetime.time(10, 50), 'Bengaluru', 551736, Decimal('8.98'), 59, 'Winter', '9A9669F8', 9, 11, 'Fe-550', 13104, 601, 4, 3, 10369, 18046)
(datetime.date(2014, 11, 17), datetime.time(11, 16), 'Kolkata', 658776, Decimal('8.92'), 71, 'Rainy', 'AE549ED2', 10, 7, 'Fe-600D', 4658, 638, 5, 3, 11421, 24958)
(datetime.date(2014, 11, 18), datetime.time(13, 12), 'Kolkata', 552807, Decimal('9.68'), 58, 'Rainy', 'F515841D', 14, 19, 'Fe-500', 9897, 1130, 2, 1, 7546, 23472)
(datetime.date(2014, 11, 19), datetime.time(11, 23), 'Kolkata', 492167, Decimal('7.27'), 72, 'Winter', '39552459', 17, 6, 'Fe-500D', 10842, 811, 1, 1, 14438, 15279)
(datetime.date(2014, 11, 20), datetime.time(17, 58), 'Pune', 582493, Decimal('8.74'), 60, 'Winter', '56E98A43', 11, 16, 'Fe-500D', 9988, 1439, 14, 3, 12212, 18033)
(datetime.date(2014, 11, 21), datetime.time(14, 55), 'Pune', 670562, Decimal('10.19'), 69, 'Rainy', '432DC322', 10, 21, 'Fe-500D', 7682, 1268, 2, 2, 13126, 18022)
(datetime.date(2014, 11, 22), datetime.time(12, 14), 'Delhi', 452342, Decimal('6.32'), 67, 'Winter', '8DA0A29C', 13, 24, 'Fe-550D', 8137, 835, 3, 7, 14878, 22841)
(datetime.date(2014, 11, 23), datetime.time(17, 9), 'Delhi', 552026, Decimal('7.74'), 65, 'Sunny', '7ABCDC2B', 10, 13, 'Fe-550', 5715, 1110, 7, 4, 7518, 16992)
(datetime.date(2014, 11, 24), datetime.time(13, 1), 'Delhi', 459220, Decimal('9.73'), 50, 'Winter', '5CAEFDAF', 11, 31, 'Fe-600D', 3794, 874, 8, 3, 10251, 24745)
(datetime.date(2014, 11, 25), datetime.time(17, 53), 'Delhi', 497144, Decimal('7.52'), 62, 'Rainy', '29144023', 12, 22, 'Fe-415', 13628, 1448, 2, 1, 13130, 22829)
(datetime.date(2014, 11, 26), datetime.time(17, 31), 'Kolkata', 551738, Decimal('8.47'), 67, 'Rainy', 'A51A297B', 10, 22, 'Fe-415', 9763, 522, 6, 1, 10667, 16852)
(datetime.date(2014, 11, 27), datetime.time(9, 35), 'Delhi', 627690, Decimal('10.04'), 69, 'Rainy', 'BE21DFD4', 30, 30, 'Fe-600D', 13504, 720, 5, 1, 11337, 23847)
(datetime.date(2014, 11, 28), datetime.time(9, 59), 'Kolkata', 619737, Decimal('10.86'), 53, 'Sunny', '97CDE880', 22, 27, 'Fe-500D', 12681, 1111, 14, 5, 12695, 19878)
(datetime.date(2014, 11, 29), datetime.time(17, 9), 'Hyderabad', 610719, Decimal('9.32'), 64, 'Rainy', '9D5EC70E', 17, 20, 'Fe-550', 5329, 752, 6, 5, 9284, 17510)
(datetime.date(2014, 11, 30), datetime.time(14, 15), 'Hyderabad', 491421, Decimal('7.13'), 67, 'Rainy', '66C6C754', 18, 31, 'Fe-500', 10198, 831, 6, 5, 11610, 20601)
(datetime.date(2014, 12, 1), datetime.time(15, 5), 'Hyderabad', 623990, Decimal('8.12'), 73, 'Rainy', 'B7EBA3DD', 17, 10, 'Fe-550', 10999, 1061, 13, 5, 14079, 21549)
(datetime.date(2014, 12, 2), datetime.time(12, 5), 'Pune', 525777, Decimal('8.31'), 69, 'Winter', '1686CC55', 26, 20, 'Fe-600D', 10219, 1396, 12, 4, 9339, 19877)
(datetime.date(2014, 12, 3), datetime.time(17, 17), 'Pune', 583836, Decimal('7.57'), 72, 'Sunny', '1E2D0559', 30, 28, 'Fe-415', 3878, 557, 10, 6, 12799, 19883)
(datetime.date(2014, 12, 4), datetime.time(15, 15), 'Bengaluru', 472591, Decimal('6.36'), 74, 'Sunny', '89AD9B77', 19, 15, 'Fe-415D', 3308, 1356, 15, 2, 8117, 16374)
(datetime.date(2014, 12, 5), datetime.time(11, 6), 'Bengaluru', 489025, Decimal('8.13'), 62, 'Rainy', '89AD9B77', 26, 7, 'Fe-550D', 10346, 1241, 2, 4, 13531, 23072)
(datetime.date(2014, 12, 6), datetime.time(15, 17), 'Hyderabad', 477470, Decimal('7.90'), 66, 'Rainy', 'DEABA8B8', 16, 16, 'Fe-500D', 8901, 741, 5, 2, 13519, 23973)
(datetime.date(2014, 12, 7), datetime.time(13, 33), 'Pune', 515122, Decimal('9.52'), 54, 'Rainy', '654785AC', 8, 16, 'Fe-600D', 13987, 1343, 14, 1, 7899, 15786)
(datetime.date(2014, 12, 8), datetime.time(9, 50), 'Delhi', 528234, Decimal('8.75'), 60, 'Rainy', '3384A891', 19, 9, 'Fe-415', 7396, 949, 13, 5, 8562, 22877)
(datetime.date(2014, 12, 9), datetime.time(9, 31), 'Delhi', 575413, Decimal('9.51'), 66, 'Winter', '12F6692C', 13, 17, 'Fe-500', 10681, 1482, 6, 3, 7209, 15997)
(datetime.date(2014, 12, 10), datetime.time(14, 4), 'Bengaluru', 466408, Decimal('6.25'), 69, 'Winter', '7A1005B4', 9, 18, 'Fe-600D', 11438, 564, 14, 7, 9550, 19116)
(datetime.date(2014, 12, 11), datetime.time(9, 4), 'Hyderabad', 538676, Decimal('7.64'), 71, 'Rainy', '0C0E7B6B', 20, 23, 'Fe-550', 12921, 998, 3, 6, 12161, 21510)
(datetime.date(2014, 12, 12), datetime.time(16, 39), 'Delhi', 598565, Decimal('7.05'), 74, 'Rainy', 'DA3C0439', 22, 19, 'Fe-550', 3569, 1346, 4, 6, 10935, 24161)
(datetime.date(2014, 12, 13), datetime.time(13, 45), 'Kolkata', 715763, Decimal('10.29'), 70, 'Winter', 'A45855B6', 10, 29, 'Fe-550D', 13644, 762, 11, 3, 13308, 23693)
(datetime.date(2014, 12, 14), datetime.time(17, 5), 'Bengaluru', 601781, Decimal('8.93'), 59, 'Winter', '698B6843', 12, 7, 'Fe-415', 7461, 1187, 7, 4, 12119, 24343)
(datetime.date(2014, 12, 15), datetime.time(17, 37), 'Hyderabad', 638660, Decimal('7.58'), 74, 'Sunny', '55D1E935', 11, 16, 'Fe-550D', 9996, 894, 10, 7, 9664, 20288)
(datetime.date(2014, 12, 16), datetime.time(15, 44), 'Kolkata', 465164, Decimal('6.35'), 71, 'Rainy', '221A235E', 8, 13, 'Fe-550D', 14500, 805, 3, 4, 7412, 22604)
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(datetime.date(2022, 11, 26), datetime.time(11, 45), 'Kolkata', 473352, Decimal('7.38'), 66, 'Rainy', 'EEB6ECAF', 14, 11, 'Fe-550', 4598, 1098, 8, 4, 7471, 15842)
(datetime.date(2022, 11, 27), datetime.time(16, 18), 'Pune', 614905, Decimal('8.59'), 68, 'Sunny', '9B62C3D3', 25, 10, 'Fe-550', 10925, 1423, 8, 4, 13169, 21242)
(datetime.date(2022, 11, 28), datetime.time(15, 4), 'Hyderabad', 516673, Decimal('6.76'), 72, 'Winter', 'C922D599', 20, 31, 'Fe-550D', 12153, 1035, 12, 1, 7571, 23207)
(datetime.date(2022, 11, 29), datetime.time(12, 50), 'Delhi', 596045, Decimal('11.49'), 50, 'Winter', '2FD96278', 14, 10, 'Fe-415D', 11630, 946, 10, 6, 12899, 19376)
(datetime.date(2022, 11, 30), datetime.time(16, 9), 'Delhi', 494497, Decimal('7.14'), 66, 'Rainy', '9903DCB4', 24, 31, 'Fe-500D', 7967, 1025, 15, 7, 13525, 20931)
(datetime.date(2022, 12, 1), datetime.time(10, 25), 'Delhi', 569823, Decimal('7.91'), 72, 'Rainy', '48E55DAB', 19, 10, 'Fe-500D', 9198, 1415, 13, 4, 13862, 17220)
(datetime.date(2022, 12, 2), datetime.time(12, 57), 'Bengaluru', 587751, Decimal('10.30'), 56, 'Winter', 'BA8E193D', 9, 29, 'Fe-415', 3564, 730, 14, 3, 12337, 19553)
(datetime.date(2022, 12, 3), datetime.time(9, 37), 'Pune', 491982, Decimal('6.80'), 69, 'Rainy', 'B4BB85C4', 23, 9, 'Fe-600', 11270, 876, 11, 7, 11629, 20661)
(datetime.date(2022, 12, 4), datetime.time(9, 10), 'Bengaluru', 451313, Decimal('7.22'), 59, 'Winter', 'DE595AA7', 27, 21, 'Fe-415D', 9877, 831, 8, 5, 14905, 22066)
(datetime.date(2022, 12, 5), datetime.time(14, 28), 'Delhi', 436562, Decimal('7.43'), 55, 'Winter', '22A1F7A2', 17, 7, 'Fe-415', 7314, 994, 14, 4, 10947, 22636)
(datetime.date(2022, 12, 6), datetime.time(14, 37), 'Delhi', 521670, Decimal('10.02'), 52, 'Rainy', '734FC3A4', 22, 7, 'Fe-600D', 3051, 764, 13, 1, 10874, 16524)
(datetime.date(2022, 12, 7), datetime.time(12, 46), 'Pune', 551974, Decimal('7.42'), 67, 'Rainy', 'D79A69E1', 20, 22, 'Fe-550D', 5113, 1422, 5, 2, 12811, 24418)
(datetime.date(2022, 12, 8), datetime.time(13, 57), 'Bengaluru', 465745, Decimal('6.90'), 63, 'Rainy', 'FEE146E7', 13, 19, 'Fe-550', 3677, 1208, 1, 7, 10778, 20991)
(datetime.date(2022, 12, 9), datetime.time(11, 6), 'Delhi', 475888, Decimal('6.80'), 70, 'Winter', 'FCE93E3B', 13, 8, 'Fe-600D', 11813, 731, 12, 5, 11795, 17412)
(datetime.date(2022, 12, 10), datetime.time(14, 55), 'Pune', 587553, Decimal('10.98'), 53, 'Rainy', '6BC84862', 15, 24, 'Fe-500D', 8013, 986, 11, 4, 14370, 20825)
(datetime.date(2022, 12, 11), datetime.time(14, 8), 'Pune', 558759, Decimal('9.23'), 63, 'Winter', 'DB8CE16B', 9, 10, 'Fe-600D', 8338, 540, 14, 6, 13120, 19119)
(datetime.date(2022, 12, 12), datetime.time(16, 41), 'Delhi', 427024, Decimal('6.41'), 65, 'Rainy', '6CD652DB', 9, 17, 'Fe-600', 12338, 1367, 13, 3, 9798, 17628)
(datetime.date(2022, 12, 13), datetime.time(17, 28), 'Bengaluru', 450560, Decimal('7.07'), 61, 'Rainy', '33DE7E4D', 29, 13, 'Fe-415', 10821, 719, 2, 6, 10357, 23534)
(datetime.date(2022, 12, 14), datetime.time(13, 44), 'Hyderabad', 470407, Decimal('6.70'), 73, 'Winter', '221A235E', 12, 20, 'Fe-600', 10815, 1494, 8, 2, 7774, 22801)
(datetime.date(2022, 12, 15), datetime.time(16, 17), 'Kolkata', 603239, Decimal('9.21'), 65, 'Winter', '54E611CB', 14, 27, 'Fe-500', 7631, 1052, 8, 3, 8088, 22070)
(datetime.date(2022, 12, 16), datetime.time(12, 19), 'Hyderabad', 498192, Decimal('6.65'), 68, 'Rainy', '88960199', 24, 18, 'Fe-550', 14194, 1273, 11, 1, 7244, 24783)
(datetime.date(2022, 12, 17), datetime.time(13, 33), 'Delhi', 604713, Decimal('9.49'), 53, 'Winter', '8BAD7BD3', 13, 8, 'Fe-550', 11475, 675, 12, 7, 13781, 20959)
(datetime.date(2022, 12, 18), datetime.time(15, 17), 'Hyderabad', 620900, Decimal('10.89'), 55, 'Winter', '1DB7945F', 19, 13, 'Fe-600D', 4673, 700, 10, 1, 14706, 18892)
(datetime.date(2022, 12, 19), datetime.time(14, 8), 'Delhi', 550777, Decimal('8.10'), 64, 'Sunny', 'A3641A87', 18, 19, 'Fe-500', 14281, 1434, 15, 3, 9242, 20641)
(datetime.date(2022, 12, 20), datetime.time(15, 6), 'Hyderabad', 671344, Decimal('11.14'), 52, 'Rainy', 'EDFA35B7', 13, 5, 'Fe-500D', 6354, 1027, 4, 7, 14151, 20393)
(datetime.date(2022, 12, 21), datetime.time(15, 56), 'Kolkata', 583549, Decimal('9.34'), 71, 'Rainy', '62C387DA', 9, 16, 'Fe-500', 12381, 1323, 5, 4, 9398, 16382)
(datetime.date(2022, 12, 22), datetime.time(13, 30), 'Kolkata', 618017, Decimal('9.92'), 60, 'Rainy', 'B12B40EE', 30, 24, 'Fe-550', 10285, 929, 2, 5, 9167, 15294)
(datetime.date(2022, 12, 23), datetime.time(17, 30), 'Pune', 631674, Decimal('9.22'), 64, 'Sunny', '9B88DF4E', 19, 8, 'Fe-500', 14474, 953, 9, 6, 10819, 19264)
(datetime.date(2022, 12, 24), datetime.time(14, 21), 'Delhi', 524959, Decimal('7.42'), 59, 'Winter', 'F8082C33', 20, 19, 'Fe-415', 12530, 634, 8, 5, 14609, 19082)
(datetime.date(2022, 12, 25), datetime.time(11, 6), 'Hyderabad', 469658, Decimal('7.47'), 57, 'Rainy', 'D214BD6B', 25, 28, 'Fe-550D', 14544, 1446, 12, 3, 8012, 21319)
(datetime.date(2022, 12, 26), datetime.time(13, 1), 'Kolkata', 525527, Decimal('6.58'), 71, 'Winter', 'E2A6C35C', 13, 19, 'Fe-600', 7275, 1084, 7, 4, 9862, 19080)
(datetime.date(2022, 12, 27), datetime.time(12, 6), 'Bengaluru', 484383, Decimal('6.62'), 73, 'Rainy', 'C2E14C41', 11, 8, 'Fe-550', 14973, 846, 9, 2, 7805, 20612)
(datetime.date(2022, 12, 28), datetime.time(14, 4), 'Bengaluru', 507091, Decimal('9.80'), 53, 'Winter', 'ED731D02', 16, 28, 'Fe-415D', 7903, 1175, 3, 5, 7481, 16232)
(datetime.date(2022, 12, 29), datetime.time(14, 46), 'Delhi', 616587, Decimal('10.80'), 61, 'Rainy', '6E18B7B2', 11, 10, 'Fe-415', 10273, 1343, 10, 5, 10319, 16242)
(datetime.date(2022, 12, 30), datetime.time(14, 29), 'Kolkata', 453449, Decimal('6.45'), 70, 'Rainy', '5C4B15E2', 20, 6, 'Fe-500D', 12683, 1089, 2, 7, 7723, 19288)
(datetime.date(2022, 12, 31), datetime.time(17, 6), 'Hyderabad', 590587, Decimal('7.82'), 60, 'Sunny', 'A6E78DD7', 23, 4, 'Fe-550D', 4850, 663, 13, 7, 8965, 24862)
(datetime.date(2023, 1, 1), datetime.time(11, 58), 'Bengaluru', 434289, Decimal('6.52'), 64, 'Winter', '16D743AC', 11, 16, 'Fe-500D', 14710, 1478, 14, 6, 9035, 24239)
(datetime.date(2023, 1, 2), datetime.time(11, 23), 'Pune', 470438, Decimal('7.29'), 67, 'Rainy', '29DCE83B', 21, 16, 'Fe-415', 6420, 1448, 11, 4, 7513, 16038)
(datetime.date(2023, 1, 3), datetime.time(12, 56), 'Bengaluru', 678203, Decimal('11.35'), 54, 'Rainy', '2AC52B0E', 14, 10, 'Fe-600D', 4361, 1077, 3, 1, 8165, 18302)
(datetime.date(2023, 1, 4), datetime.time(13, 11), 'Bengaluru', 432735, Decimal('6.65'), 62, 'Winter', 'B8CCC3F8', 12, 9, 'Fe-550D', 3959, 817, 14, 2, 14410, 18835)
(datetime.date(2023, 1, 5), datetime.time(11, 2), 'Bengaluru', 601130, Decimal('10.37'), 58, 'Sunny', 'DC1D8CD9', 17, 30, 'Fe-415D', 10988, 1190, 9, 6, 10048, 15359)
(datetime.date(2023, 1, 6), datetime.time(9, 1), 'Delhi', 716981, Decimal('13.64'), 60, 'Sunny', '1AF4A763', 32, 15, 'Fe-500', 4921, 847, 3, 2, 11258, 16584)
(datetime.date(2023, 1, 7), datetime.time(12, 28), 'Bengaluru', 474580, Decimal('7.60'), 68, 'Winter', 'CC3A9383', 10, 23, 'Fe-500', 3696, 531, 5, 7, 9187, 23592)
(datetime.date(2023, 1, 8), datetime.time(17, 58), 'Hyderabad', 557262, Decimal('7.83'), 72, 'Rainy', '99C16FC0', 9, 16, 'Fe-500', 11860, 763, 11, 4, 7650, 24581)
(datetime.date(2023, 1, 9), datetime.time(10, 37), 'Delhi', 513212, Decimal('8.38'), 63, 'Rainy', '779E8D4E', 19, 10, 'Fe-415D', 11553, 1093, 9, 6, 11133, 18668)
(datetime.date(2023, 1, 10), datetime.time(14, 21), 'Kolkata', 664301, Decimal('11.38'), 58, 'Rainy', 'D789C4F8', 32, 24, 'Fe-500', 10676, 760, 8, 7, 9258, 21808)
(datetime.date(2023, 1, 11), datetime.time(11, 32), 'Pune', 452221, Decimal('6.44'), 68, 'Sunny', '111786F3', 17, 30, 'Fe-500', 13691, 588, 10, 3, 12355, 18617)
(datetime.date(2023, 1, 12), datetime.time(11, 58), 'Kolkata', 647057, Decimal('9.48'), 66, 'Sunny', '66260859', 22, 19, 'Fe-600D', 3927, 818, 2, 5, 13927, 23045)
(datetime.date(2023, 1, 13), datetime.time(11, 56), 'Hyderabad', 553290, Decimal('8.64'), 63, 'Winter', 'CE573A11', 18, 11, 'Fe-415D', 3177, 1290, 6, 6, 9016, 18321)
(datetime.date(2023, 1, 14), datetime.time(9, 39), 'Kolkata', 508059, Decimal('6.49'), 69, 'Rainy', '734FC3A4', 9, 22, 'Fe-550', 8491, 1283, 8, 3, 11692, 19754)
(datetime.date(2023, 1, 15), datetime.time(14, 29), 'Hyderabad', 506232, Decimal('8.47'), 56, 'Rainy', '4126B2B8', 17, 21, 'Fe-415', 14234, 1403, 13, 5, 11495, 23771)
(datetime.date(2023, 1, 16), datetime.time(13, 11), 'Delhi', 566922, Decimal('8.34'), 60, 'Rainy', '16D743AC', 26, 12, 'Fe-500', 14459, 523, 7, 5, 8896, 15404)
(datetime.date(2023, 1, 17), datetime.time(17, 53), 'Delhi', 633624, Decimal('11.14'), 52, 'Sunny', 'AA668217', 14, 20, 'Fe-600', 9623, 1290, 12, 2, 14784, 16252)
(datetime.date(2023, 1, 18), datetime.time(13, 14), 'Bengaluru', 519540, Decimal('8.62'), 67, 'Winter', '8BAD7BD3', 29, 20, 'Fe-415D', 14239, 1070, 5, 3, 10764, 17380)
(datetime.date(2023, 1, 19), datetime.time(17, 45), 'Kolkata', 604895, Decimal('9.16'), 71, 'Sunny', '98398215', 20, 24, 'Fe-500', 5946, 1296, 4, 1, 10824, 22045)
(datetime.date(2023, 1, 20), datetime.time(14, 4), 'Hyderabad', 624260, Decimal('11.39'), 50, 'Winter', 'E7BD53A3', 14, 20, 'Fe-415D', 14825, 1077, 1, 5, 14963, 16023)
(datetime.date(2023, 1, 21), datetime.time(13, 44), 'Bengaluru', 486670, Decimal('7.81'), 64, 'Winter', 'EBFB936D', 15, 26, 'Fe-500', 11236, 967, 2, 4, 7039, 15570)
(datetime.date(2023, 1, 22), datetime.time(14, 41), 'Kolkata', 456547, Decimal('7.48'), 55, 'Rainy', '28A3282B', 30, 31, 'Fe-415D', 10833, 567, 9, 6, 7515, 24444)
(datetime.date(2023, 1, 23), datetime.time(15, 9), 'Hyderabad', 584972, Decimal('8.91'), 67, 'Rainy', '65B33CE5', 17, 21, 'Fe-500', 3046, 1304, 3, 4, 11258, 17106)
(datetime.date(2023, 1, 24), datetime.time(11, 23), 'Delhi', 523810, Decimal('7.05'), 72, 'Winter', '3411F5D8', 11, 30, 'Fe-550', 14302, 1423, 2, 3, 10789, 23619)
(datetime.date(2023, 1, 25), datetime.time(17, 8), 'Delhi', 712491, Decimal('8.93'), 74, 'Sunny', 'D66869B7', 26, 8, 'Fe-550', 6995, 1023, 11, 4, 13897, 15951)
(datetime.date(2023, 1, 26), datetime.time(11, 15), 'Hyderabad', 447397, Decimal('8.29'), 57, 'Winter', '5C89CE5C', 10, 17, 'Fe-500', 3612, 1386, 12, 4, 7215, 18325)
(datetime.date(2023, 1, 27), datetime.time(16, 39), 'Hyderabad', 645444, Decimal('8.93'), 71, 'Rainy', '5D15BA24', 11, 24, 'Fe-600D', 9971, 1206, 3, 4, 11165, 21228)
(datetime.date(2023, 1, 28), datetime.time(14, 41), 'Bengaluru', 639487, Decimal('9.44'), 68, 'Sunny', 'C993D9D8', 9, 30, 'Fe-500', 5976, 1432, 13, 6, 13497, 15918)
(datetime.date(2023, 1, 29), datetime.time(9, 35), 'Bengaluru', 476261, Decimal('5.97'), 74, 'Winter', 'E7BD53A3', 20, 21, 'Fe-550', 10366, 1176, 14, 4, 8365, 20727)
(datetime.date(2023, 1, 30), datetime.time(16, 3), 'Bengaluru', 566828, Decimal('8.47'), 68, 'Winter', '67A4B965', 12, 21, 'Fe-600D', 14055, 537, 12, 1, 14818, 24721)
(datetime.date(2023, 1, 31), datetime.time(13, 38), 'Hyderabad', 457875, Decimal('6.56'), 72, 'Rainy', 'C7935D50', 18, 27, 'Fe-550D', 14844, 1216, 11, 7, 7262, 16842)
(datetime.date(2023, 2, 1), datetime.time(15, 50), 'Kolkata', 513495, Decimal('7.38'), 72, 'Sunny', 'DA62EEEA', 23, 26, 'Fe-600D', 9021, 1280, 11, 6, 10327, 21829)
(datetime.date(2023, 2, 2), datetime.time(17, 53), 'Bengaluru', 536832, Decimal('9.85'), 50, 'Winter', '04876EC9', 19, 11, 'Fe-600', 14614, 811, 13, 2, 8170, 20278)
(datetime.date(2023, 2, 3), datetime.time(15, 8), 'Kolkata', 581601, Decimal('9.35'), 59, 'Rainy', 'F9A6BD94', 21, 26, 'Fe-500D', 12411, 1256, 14, 5, 10039, 21770)
(datetime.date(2023, 2, 4), datetime.time(17, 19), 'Bengaluru', 635799, Decimal('11.83'), 52, 'Rainy', '5E424CEE', 10, 10, 'Fe-550', 6227, 1120, 10, 3, 7524, 24065)
(datetime.date(2023, 2, 5), datetime.time(15, 17), 'Bengaluru', 452628, Decimal('6.01'), 73, 'Rainy', '4126B2B8', 15, 20, 'Fe-550D', 14912, 1331, 8, 4, 8802, 21758)
(datetime.date(2023, 2, 6), datetime.time(13, 59), 'Delhi', 717000, Decimal('13.89'), 56, 'Rainy', '3138A368', 12, 31, 'Fe-600D', 9859, 1235, 1, 2, 10945, 20471)
(datetime.date(2023, 2, 7), datetime.time(16, 51), 'Pune', 443535, Decimal('6.96'), 62, 'Winter', 'DB8CE16B', 17, 31, 'Fe-500D', 7031, 558, 13, 6, 8988, 17890)
(datetime.date(2023, 2, 8), datetime.time(17, 52), 'Bengaluru', 447966, Decimal('8.19'), 62, 'Winter', 'E2A6C35C', 23, 27, 'Fe-415D', 10121, 1270, 6, 6, 13405, 23388)
(datetime.date(2023, 2, 9), datetime.time(9, 9), 'Kolkata', 645836, Decimal('10.39'), 57, 'Winter', '45A6870C', 10, 9, 'Fe-415', 10810, 1176, 10, 4, 8458, 15082)
(datetime.date(2023, 2, 10), datetime.time(16, 4), 'Pune', 597303, Decimal('10.22'), None, 'Winter', 'B7F846A1', 9, 5, 'Fe-600', 3365, 667, 9, 1, 12127, 23338)
(datetime.date(2023, 2, 11), datetime.time(9, 31), 'Delhi', 613579, Decimal('9.32'), 61, 'Rainy', 'C3DBE609', 29, 5, 'Fe-600D', 10901, 1021, 3, 2, 14163, 24689)
(datetime.date(2023, 2, 12), datetime.time(16, 26), 'Bengaluru', 471603, Decimal('7.14'), 67, 'Rainy', 'A2DBA5A9', 25, 23, 'Fe-600D', 8514, 914, 1, 3, 14120, 20867)
(datetime.date(2023, 2, 13), datetime.time(11, 4), 'Bengaluru', 532100, Decimal('6.75'), 64, 'Winter', '99EBABB2', 15, 32, 'Fe-550', 7513, 594, 12, 7, 12031, 20428)
(datetime.date(2023, 2, 14), datetime.time(10, 46), 'Bengaluru', 706813, Decimal('11.33'), 68, 'Sunny', '781BB88C', 16, 25, 'Fe-500', 7930, 582, 1, 6, 12967, 15310)
(datetime.date(2023, 2, 15), datetime.time(9, 5), 'Kolkata', 490818, Decimal('7.19'), 64, 'Rainy', '48DF1CC8', 29, 28, 'Fe-550D', 9246, 1010, 9, 5, 8255, 24480)
(datetime.date(2023, 2, 16), datetime.time(14, 31), 'Delhi', 575223, Decimal('9.79'), 55, 'Winter', 'B33D397A', 9, 12, 'Fe-500', 14403, 669, 11, 5, 12290, 21161)
(datetime.date(2023, 2, 17), datetime.time(15, 20), 'Hyderabad', 512005, Decimal('8.15'), 69, 'Rainy', '7EE45AE3', 8, 14, 'Fe-550', 5684, 1245, 13, 4, 11889, 18381)
(datetime.date(2023, 2, 18), datetime.time(16, 57), 'Hyderabad', 584911, Decimal('9.64'), 57, 'Rainy', '81F1306F', 19, 26, 'Fe-500', 10342, 615, 8, 7, 9319, 18249)
(datetime.date(2023, 2, 19), datetime.time(9, 44), 'Pune', 636793, Decimal('10.53'), 56, 'Winter', '006C18BA', 13, 9, 'Fe-600D', 8063, 946, 14, 5, 8045, 18750)
(datetime.date(2023, 2, 20), datetime.time(10, 20), 'Delhi', 535270, Decimal('8.33'), 63, 'Rainy', 'A1116D6A', 18, 9, 'Fe-415', 12493, 1425, 11, 4, 14660, 17704)
(datetime.date(2023, 2, 21), datetime.time(11, 40), 'Kolkata', 492136, Decimal('7.23'), 69, 'Winter', '14096571', 11, 24, 'Fe-550', 9577, 927, 8, 2, 10973, 20331)
(datetime.date(2023, 2, 22), datetime.time(12, 43), 'Kolkata', 622375, Decimal('10.54'), 60, 'Winter', '0EDC9A3C', 11, 21, 'Fe-415D', 8055, 835, 12, 6, 11432, 17359)
(datetime.date(2023, 2, 23), datetime.time(17, 45), 'Bengaluru', 512743, Decimal('9.38'), 51, 'Sunny', '97CDE880', 8, 24, 'Fe-600D', 14095, 1224, 13, 3, 11388, 16293)
(datetime.date(2023, 2, 24), datetime.time(12, 51), 'Pune', 509579, Decimal('9.13'), 52, 'Winter', '94CE748E', 9, 19, 'Fe-500D', 3695, 1206, 14, 6, 7434, 17790)
(datetime.date(2023, 2, 25), datetime.time(11, 8), 'Delhi', 612511, Decimal('9.99'), 53, 'Winter', 'EE75A871', 16, 27, 'Fe-600D', 7225, 1492, 8, 4, 10847, 22642)
(datetime.date(2023, 2, 26), datetime.time(17, 19), 'Kolkata', 672718, Decimal('8.84'), 70, 'Sunny', 'D3B1B31D', 19, 18, 'Fe-550D', 12102, 609, 8, 1, 12405, 23116)
(datetime.date(2023, 2, 27), datetime.time(15, 32), 'Kolkata', 480401, Decimal('7.64'), 66, 'Winter', '1A6708FC', 26, 20, None, 13109, 805, 5, 7, 13980, 19983)
(datetime.date(2023, 2, 28), datetime.time(15, 46), 'Kolkata', 714667, Decimal('10.61'), None, 'Sunny', 'E6D78E95', 26, 29, 'Fe-600D', 14028, 737, 3, 2, 9064, 16895)
data.head()
| Date | Time | Location | Sales_in_Rs_per_T | Sales_volume_in_Tonnes | Price_per_kg | Climate | Customer_ID | Diameter | Length | Grade | Current_stock | Re_order | Lead_time | Production_time | Units_Produced | Production_cost | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1995-10-14 | 11:08:00 | Pune | 424440.0 | 7.07 | 60.0 | Rainy | C9D3791C | 32.0 | 13 | Fe-500 | 10770 | 618 | 7 | 3 | 14828 | 18734 |
| 1 | 1995-10-15 | 10:07:00 | Hyderabad | 426000.0 | 8.35 | 51.0 | Rainy | F515841D | 12.0 | 7 | Fe-415 | 8039 | 1195 | 2 | 3 | 12795 | 21294 |
| 2 | 1995-10-16 | 17:25:00 | Kolkata | 429360.0 | 6.82 | 63.0 | Rainy | B387F82E | 12.0 | 9 | Fe-500D | 7328 | 1372 | 14 | 6 | 13576 | 19759 |
| 3 | 1995-10-17 | 10:00:00 | Bengaluru | 432000.0 | 6.86 | 63.0 | Rainy | A8EBB29A | 8.0 | 25 | Fe-550 | 3040 | 688 | 14 | 2 | 8060 | 23073 |
| 4 | 1995-10-18 | 16:39:00 | Delhi | 432000.0 | 7.45 | 58.0 | Rainy | DC1D8CD9 | 32.0 | 27 | Fe-500 | 9011 | 704 | 11 | 6 | 7532 | 19986 |
data.tail() # Reading the last 5 columns
| Date | Time | Location | Sales_in_Rs_per_T | Sales_volume_in_Tonnes | Price_per_kg | Climate | Customer_ID | Diameter | Length | Grade | Current_stock | Re_order | Lead_time | Production_time | Units_Produced | Production_cost | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 9995 | 2023-02-24 | 12:51:00 | Pune | 509579.0 | 9.13 | 52.0 | Winter | 94CE748E | 9.0 | 19 | Fe-500D | 3695 | 1206 | 14 | 6 | 7434 | 17790 |
| 9996 | 2023-02-25 | 11:08:00 | Delhi | 612511.0 | 9.99 | 53.0 | Winter | EE75A871 | 16.0 | 27 | Fe-600D | 7225 | 1492 | 8 | 4 | 10847 | 22642 |
| 9997 | 2023-02-26 | 17:19:00 | Kolkata | 672718.0 | 8.84 | 70.0 | Sunny | D3B1B31D | 19.0 | 18 | Fe-550D | 12102 | 609 | 8 | 1 | 12405 | 23116 |
| 9998 | 2023-02-27 | 15:32:00 | Kolkata | 480401.0 | 7.64 | 66.0 | Winter | 1A6708FC | 26.0 | 20 | None | 13109 | 805 | 5 | 7 | 13980 | 19983 |
| 9999 | 2023-02-28 | 15:46:00 | Kolkata | 714667.0 | 10.61 | NaN | Sunny | E6D78E95 | 26.0 | 29 | Fe-600D | 14028 | 737 | 3 | 2 | 9064 | 16895 |
data.shape
(10000, 17)
from datetime import datetime
start_date = datetime(1995, 10, 14)
end_date = datetime(2023, 2, 28)
delta = end_date - start_date
years = delta.days // 365
months = (delta.days % 365) // 30
print("Data covers {} years and {} months".format(years, months))
Data covers 27 years and 4 months
data.shape # There are 10000 rows and 17 columns
(10000, 17)
# let's see what are the list of columns exist in our dataset
data.columns
Index(['Date', 'Time', 'Location', 'Sales_in_Rs_per_T',
'Sales_volume_in_Tonnes', 'Price_per_kg', 'Climate', 'Customer_ID',
'Diameter', 'Length', 'Grade', 'Current_stock', 'Re_order', 'Lead_time',
'Production_time', 'Units_Produced', 'Production_cost'],
dtype='object')
# Descriptive Statistics for numerical columns
data.describe().T
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Sales_in_Rs_per_T | 9992.0 | 541320.140112 | 75223.368004 | 4158.0 | 481218.00 | 536157.0 | 594403.75 | 717230.0 |
| Price_per_kg | 9995.0 | 62.958179 | 6.472928 | 50.0 | 58.00 | 63.0 | 68.00 | 74.0 |
| Diameter | 9998.0 | 16.974995 | 6.467049 | 8.0 | 12.00 | 16.0 | 21.00 | 32.0 |
| Length | 10000.0 | 17.930300 | 8.123869 | 4.0 | 11.00 | 18.0 | 25.00 | 32.0 |
| Current_stock | 10000.0 | 8991.488900 | 3420.646321 | 3003.0 | 6045.75 | 9080.0 | 11919.50 | 14989.0 |
| Re_order | 10000.0 | 999.154800 | 285.957747 | 502.0 | 753.00 | 999.0 | 1245.00 | 1500.0 |
| Lead_time | 10000.0 | 8.011000 | 4.049953 | 1.0 | 5.00 | 8.0 | 11.00 | 15.0 |
| Production_time | 10000.0 | 4.035200 | 1.783726 | 1.0 | 3.00 | 4.0 | 6.00 | 7.0 |
| Units_Produced | 10000.0 | 10921.567000 | 2288.349041 | 6017.0 | 8957.75 | 10886.5 | 12883.00 | 15800.0 |
| Production_cost | 10000.0 | 20079.277400 | 2854.058653 | 15004.0 | 17670.75 | 20150.5 | 22524.00 | 25000.0 |
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 10000 entries, 0 to 9999 Data columns (total 17 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Date 10000 non-null object 1 Time 9989 non-null object 2 Location 9994 non-null object 3 Sales_in_Rs_per_T 9992 non-null float64 4 Sales_volume_in_Tonnes 9992 non-null object 5 Price_per_kg 9995 non-null float64 6 Climate 9991 non-null object 7 Customer_ID 10000 non-null object 8 Diameter 9998 non-null float64 9 Length 10000 non-null int64 10 Grade 9995 non-null object 11 Current_stock 10000 non-null int64 12 Re_order 10000 non-null int64 13 Lead_time 10000 non-null int64 14 Production_time 10000 non-null int64 15 Units_Produced 10000 non-null int64 16 Production_cost 10000 non-null int64 dtypes: float64(3), int64(7), object(7) memory usage: 1.3+ MB
# Convert the "Sales_volume_in_Tonnes" column to float64
data["Sales_volume_in_Tonnes"] = data["Sales_volume_in_Tonnes"].astype(float)
data["Sales_volume_in_Tonnes"]
0 7.07
1 8.35
2 6.82
3 6.86
4 7.45
...
9995 9.13
9996 9.99
9997 8.84
9998 7.64
9999 10.61
Name: Sales_volume_in_Tonnes, Length: 10000, dtype: float64
#Checking how many NULL or Missing Values in our dataset
null_cols = data.columns[data.isnull().any()].tolist()
print("Columns with missing values:", null_cols)
Columns with missing values: ['Time', 'Location', 'Sales_in_Rs_per_T', 'Sales_volume_in_Tonnes', 'Price_per_kg', 'Climate', 'Diameter', 'Grade']
duplicates = data.duplicated()
print("Number of duplicate rows: ", duplicates.sum()) # There are NO duplicataes values in our dataset
Number of duplicate rows: 0
# Converting a column of strings in object format to datetime format
data["Date"] = pd.to_datetime(data["Date"])
data.dtypes
Date datetime64[ns] Time object Location object Sales_in_Rs_per_T float64 Sales_volume_in_Tonnes float64 Price_per_kg float64 Climate object Customer_ID object Diameter float64 Length int64 Grade object Current_stock int64 Re_order int64 Lead_time int64 Production_time int64 Units_Produced int64 Production_cost int64 dtype: object
# Renaming column names for better understanding and analysis purpose
data.rename(columns = {'Sales_in_Rs_per_T': 'Sales', 'Sales_volume_in_Tonnes': 'Sales_volume',
'Price_per_kg': 'Price/Kg', 'Current stock': 'Current_stock',
'Lead time': 'Lead_time', 'Production time': 'Production_time',
'Units Produced ': 'Units_Produced ', 'Production cost': 'Production_cost'}, inplace = True)
data.columns
Index(['Date', 'Time', 'Location', 'Sales', 'Sales_volume', 'Price/Kg',
'Climate', 'Customer_ID', 'Diameter', 'Length', 'Grade',
'Current_stock', 'Re_order', 'Lead_time', 'Production_time',
'Units_Produced', 'Production_cost'],
dtype='object')
# Reseting the index to Date column
data.set_index("Date", inplace = True)
data.head(2)
| Time | Location | Sales | Sales_volume | Price/Kg | Climate | Customer_ID | Diameter | Length | Grade | Current_stock | Re_order | Lead_time | Production_time | Units_Produced | Production_cost | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Date | ||||||||||||||||
| 1995-10-14 | 11:08:00 | Pune | 424440.0 | 7.07 | 60.0 | Rainy | C9D3791C | 32.0 | 13 | Fe-500 | 10770 | 618 | 7 | 3 | 14828 | 18734 |
| 1995-10-15 | 10:07:00 | Hyderabad | 426000.0 | 8.35 | 51.0 | Rainy | F515841D | 12.0 | 7 | Fe-415 | 8039 | 1195 | 2 | 3 | 12795 | 21294 |
# Checking for unique values count for all columns
unique_count = data.nunique()
print(unique_count)
Time 450 Location 5 Sales 9613 Sales_volume 726 Price/Kg 25 Climate 3 Customer_ID 1001 Diameter 25 Length 29 Grade 8 Current_stock 6818 Re_order 999 Lead_time 15 Production_time 7 Units_Produced 5694 Production_cost 6305 dtype: int64
numeric_features = data.select_dtypes(include = ['int64', 'float64']).columns
numeric_features
Index(['Sales', 'Sales_volume', 'Price/Kg', 'Diameter', 'Length',
'Current_stock', 'Re_order', 'Lead_time', 'Production_time',
'Units_Produced', 'Production_cost'],
dtype='object')
numeric_features.value_counts().sum()
11
categorical_features = data.select_dtypes(exclude = ['int64', 'float64']).columns
categorical_features
Index(['Time', 'Location', 'Climate', 'Customer_ID', 'Grade'], dtype='object')
categorical_features.value_counts().sum()
5
data["Sales_volume"].min()
4.1
data["Sales_volume"].max()
15.6
# Calculate 1.5*IQR
q1 = data['Sales_volume'].quantile(0.25)
q3 = data['Sales_volume'].quantile(0.75)
iqr = q3 - q1
upper = q3 + 1.5*iqr
# Count outliers
outliers = data[data['Sales_volume'] > upper]
num_outliers = len(outliers)
print(f"Number of outliers: {num_outliers}")
Number of outliers: 51
outliers = data[data['Sales_volume'] > upper]
outlier_dates_sales = data.loc[outliers.index, ['Sales_volume']]
print(outlier_dates_sales)
Sales_volume Date 1995-10-25 15.10 1995-11-30 15.20 1996-03-23 15.30 1996-09-20 13.97 1996-11-13 15.40 1997-01-18 13.19 1997-01-25 13.17 1997-02-04 13.09 1997-02-11 13.31 1997-02-14 13.14 1997-02-16 13.41 1997-02-22 13.65 1997-02-26 15.60 1998-07-10 12.90 1999-03-23 13.80 2000-09-04 12.95 2001-06-11 12.93 2001-06-21 13.32 2001-07-23 13.25 2003-03-10 13.52 2005-01-26 13.24 2005-02-20 13.20 2005-08-04 13.84 2005-08-07 13.92 2005-08-18 12.99 2007-06-29 13.66 2008-05-18 13.93 2008-06-10 13.42 2009-12-15 13.30 2010-06-29 12.96 2010-07-02 13.24 2010-09-10 13.12 2011-01-14 13.50 2011-07-21 13.02 2012-01-26 12.87 2012-05-27 13.80 2012-08-24 12.89 2012-10-30 13.41 2013-02-12 13.76 2017-09-21 13.26 2017-10-12 12.99 2017-10-18 13.86 2019-05-09 13.03 2019-07-09 12.93 2019-08-25 13.13 2019-09-24 13.60 2021-07-09 12.90 2021-11-06 13.31 2022-11-05 13.09 2023-01-06 13.64 2023-02-06 13.89
import matplotlib.pyplot as plt
# Plot the original data
plt.plot(data.index, data["Sales_volume"], label = "Original Data")
# Plot the outlier dates and sales volumes
plt.scatter(outlier_dates_sales.index, outlier_dates_sales["Sales_volume"],
color = "red", label = "Outliers")
plt.xlabel("Date")
plt.ylabel("Sales Volume")
plt.legend()
plt.show()
# Count the number of occurrences of each year in the outlier_dates_sales index
year_counts = outlier_dates_sales.index.year.value_counts()
# Print the year-wise count of outliers
for year, count in year_counts.items():
print(f"{year} - {count} times")
1997 - 8 times 2005 - 5 times 2019 - 4 times 2012 - 4 times 2010 - 3 times 2017 - 3 times 2001 - 3 times 1996 - 3 times 1995 - 2 times 2021 - 2 times 2011 - 2 times 2008 - 2 times 2023 - 2 times 2009 - 1 times 2007 - 1 times 2003 - 1 times 2013 - 1 times 2000 - 1 times 1999 - 1 times 1998 - 1 times 2022 - 1 times
outlier_dates_sales = outlier_dates_sales.reset_index()
outlier_dates_sales['Year'] = outlier_dates_sales['Date'].dt.year
outlier_count = outlier_dates_sales.groupby('Year')['Sales_volume'].count()
print(outlier_count)
Year 1995 2 1996 3 1997 8 1998 1 1999 1 2000 1 2001 3 2003 1 2005 5 2007 1 2008 2 2009 1 2010 3 2011 2 2012 4 2013 1 2017 3 2019 4 2021 2 2022 1 2023 2 Name: Sales_volume, dtype: int64
# Count the number of missing values per year in the Sales_volume column
missing_values_per_year = data["Sales_volume"].groupby(data.index.year).apply(lambda x: x.isna().sum())
# Print the result
print(missing_values_per_year)
Date 1995 0 1996 0 1997 0 1998 4 1999 2 2000 1 2001 1 2002 0 2003 0 2004 0 2005 0 2006 0 2007 0 2008 0 2009 0 2010 0 2011 0 2012 0 2013 0 2014 0 2015 0 2016 0 2017 0 2018 0 2019 0 2020 0 2021 0 2022 0 2023 0 Name: Sales_volume, dtype: int64
# Select rows where the 'Sales volume in Tonnes' column has a missing value
missing_sales = data[data['Sales_volume'].isna()]['Sales_volume']
print(missing_sales)
Date 1998-04-13 NaN 1998-06-20 NaN 1998-09-09 NaN 1998-12-26 NaN 1999-06-23 NaN 1999-12-12 NaN 2000-06-25 NaN 2001-03-06 NaN Name: Sales_volume, dtype: float64
results = []
for col in numeric_features:
result = {
'Feature': col,
'Mean': data[col].mean(),
'Median': data[col].median(),
'Mode': data[col].mode().tolist(),
'Range': data[col].max() - data[col].min(),
'Variance': data[col].var(),
'Standard Deviation': data[col].std(),
'Skewness': data[col].skew(),
'Kurtosis': data[col].kurtosis()
}
results.append(result)
EDA = pd.DataFrame(results)
display(EDA)
| Feature | Mean | Median | Mode | Range | Variance | Standard Deviation | Skewness | Kurtosis | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Sales | 541320.140112 | 536157.00 | [433560.0] | 713072.0 | 5.658555e+09 | 75223.368004 | 0.086901 | 0.547886 |
| 1 | Sales_volume | 8.546244 | 8.43 | [7.92, 8.35] | 11.5 | 2.344348e+00 | 1.531126 | 0.468308 | -0.086608 |
| 2 | Price/Kg | 62.958179 | 63.00 | [62.0] | 24.0 | 4.189879e+01 | 6.472928 | -0.159762 | -1.006872 |
| 3 | Diameter | 16.974995 | 16.00 | [10.0] | 24.0 | 4.182272e+01 | 6.467049 | 0.589913 | -0.605864 |
| 4 | Length | 17.930300 | 18.00 | [7] | 28.0 | 6.599724e+01 | 8.123869 | 0.015827 | -1.201782 |
| 5 | Current_stock | 8991.488900 | 9080.00 | [3276, 5909, 10610] | 11986.0 | 1.170082e+07 | 3420.646321 | -0.015762 | -1.184977 |
| 6 | Re_order | 999.154800 | 999.00 | [798, 913, 1181, 1247] | 998.0 | 8.177183e+04 | 285.957747 | 0.005727 | -1.173889 |
| 7 | Lead_time | 8.011000 | 8.00 | [8] | 14.0 | 1.640212e+01 | 4.049953 | 0.002804 | -1.159690 |
| 8 | Production_time | 4.035200 | 4.00 | [6] | 6.0 | 3.181679e+00 | 1.783726 | -0.037894 | -1.091210 |
| 9 | Units_Produced | 10921.567000 | 10886.50 | [10189] | 9783.0 | 5.236541e+06 | 2288.349041 | 0.031534 | -1.180364 |
| 10 | Production_cost | 20079.277400 | 20150.50 | [17479, 20862, 23534, 23555, 23627, 24896] | 9996.0 | 8.145651e+06 | 2854.058653 | -0.042865 | -1.173977 |
# Calculate Skewness and Kurtosis
Skewness = data.skew()
Kurtosis = data.kurtosis()
# Plot histograms and add labels
for column in numeric_features:
fig, ax = plt.subplots()
ax.hist(data[column], bins = 20)
ax.axvline(data[column].mean(), color='r', linestyle='--', label='Mean')
ax.axvline(data[column].median(), color='g', linestyle='--', label='Median')
ax.axvline(data[column].mode()[0], color='b', linestyle='--', label='Mode')
ax.axvline(data[column].min(), color='y', linestyle='--', label='Min')
ax.axvline(data[column].max(), color='m', linestyle='--', label='Max')
ax.legend()
ax.set_title(column)
ax.set_xlabel('Value')
ax.set_ylabel('Frequency')
# Adding Mean, Median, Min, Max, Skewness, and Kurtosis values to the plot
ax.text(data[column].mean(), ax.get_ylim()[1]*0.9, f'Mean: {data[column].mean():.2f}', ha='center', fontsize=10)
ax.text(data[column].median(), ax.get_ylim()[1]*0.8, f'Median: {data[column].median():.2f}', ha='center', fontsize=10)
ax.text(data[column].mode()[0], ax.get_ylim()[1]*0.7, f'Mode: {data[column].mode()[0]:.2f}', ha='center', fontsize=10)
ax.text(data[column].min(), ax.get_ylim()[1]*0.6, f'Min: {data[column].min():.2f}', ha='center', fontsize=10)
ax.text(data[column].max(), ax.get_ylim()[1]*0.5, f'Max: {data[column].max():.2f}', ha='center', fontsize=10)
ax.text(data[column].mean(), ax.get_ylim()[1]*0.4, f'Skewness: {Skewness[column]:.2f}', ha='center', fontsize=10)
ax.text(data[column].mean(), ax.get_ylim()[1]*0.3, f'Kurtosis: {Kurtosis[column]:.2f}', ha='center', fontsize=10)
plt.show()
# plotting time series plot for "Sales volume in Tonnes"
plt.figure(figsize = (12, 6))
plt.plot(data.index, data["Sales_volume"])
plt.xlabel("Date")
plt.ylabel("Sales_volume")
plt.title("Time Series Plot of Sales volume in Tonnes")
plt.show()
# Creating a heatmap
plt.figure(figsize = (15,10))
sns.heatmap(data.corr(), annot = True, cmap = 'coolwarm')
<AxesSubplot: >
## Auto-EDA
import sweetviz as sv
s = sv.analyze(data)
s.show_notebook()
| | [ 0%] 00:00 ->…
# Select rows where the 'Sales volume in Tonnes' column has a missing value
missing_sales = data[data['Sales_volume'].isna()]['Sales_volume']
print(missing_sales)
Date 1998-04-13 NaN 1998-06-20 NaN 1998-09-09 NaN 1998-12-26 NaN 1999-06-23 NaN 1999-12-12 NaN 2000-06-25 NaN 2001-03-06 NaN Name: Sales_volume, dtype: float64
missing_sales.isna().sum()
8
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
import joblib
# Separate Sales and Sales_volume from other columns
numeric_features1 = ['Sales', 'Sales_volume']
numeric_features1
['Sales', 'Sales_volume']
# Separate Sales and Sales_volume from other columns
numeric_features2 = ['Price/Kg', 'Diameter', 'Length',
'Current_stock', 'Re_order', 'Lead_time', 'Production_time',
'Units_Produced', 'Production_cost']
numeric_features2
['Price/Kg', 'Diameter', 'Length', 'Current_stock', 'Re_order', 'Lead_time', 'Production_time', 'Units_Produced', 'Production_cost']
categorical_features = ['Location', 'Climate', 'Grade']
categorical_features
['Location', 'Climate', 'Grade']
from sklearn.compose import ColumnTransformer
from sklearn_pandas import DataFrameMapper
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.base import TransformerMixin, BaseEstimator
class ForwardFillImputer(TransformerMixin, BaseEstimator):
def __init__(self, features):
self.features = features
def fit(self, X, y=None):
return self
def transform(self, X):
X[self.features] = X[self.features].fillna(method = 'ffill')
return X
# Create pipelines for numeric and categorical columns
numeric_pipeline1 = Pipeline(steps = [('imputer', ForwardFillImputer(numeric_features1))])
numeric_pipeline2 = Pipeline(steps = [('imputer', ForwardFillImputer(numeric_features2))])
categorical_pipeline = Pipeline(steps = [('imputer', ForwardFillImputer(categorical_features))])
from sklearn.compose import ColumnTransformer
# Use ColumnTransformer to apply pipelines to specific columns
preprocessor = ColumnTransformer(transformers=[('num1', numeric_pipeline1, numeric_features1),
('num2', numeric_pipeline2, numeric_features2),
('cat', categorical_pipeline, categorical_features)])
# Preprocess Sales and Sales_volume data
imputation_data = preprocessor.fit_transform(data)
# Save preprocessor using joblib
import joblib
joblib.dump(preprocessor, 'Impute.joblib')
['Impute.joblib']
column_names = numeric_features1 + numeric_features2 + categorical_features
imputation_data_df = pd.DataFrame(imputation_data, columns=column_names)
print(imputation_data_df.head())
Sales Sales_volume Price/Kg Diameter Length Current_stock Re_order \
0 424440.0 7.07 60.0 32.0 13.0 10770.0 618.0
1 426000.0 8.35 51.0 12.0 7.0 8039.0 1195.0
2 429360.0 6.82 63.0 12.0 9.0 7328.0 1372.0
3 432000.0 6.86 63.0 8.0 25.0 3040.0 688.0
4 432000.0 7.45 58.0 32.0 27.0 9011.0 704.0
Lead_time Production_time Units_Produced Production_cost Location Climate \
0 7.0 3.0 14828.0 18734.0 Pune Rainy
1 2.0 3.0 12795.0 21294.0 Hyderabad Rainy
2 14.0 6.0 13576.0 19759.0 Kolkata Rainy
3 14.0 2.0 8060.0 23073.0 Bengaluru Rainy
4 11.0 6.0 7532.0 19986.0 Delhi Rainy
Grade
0 Fe-500
1 Fe-415
2 Fe-500D
3 Fe-550
4 Fe-500
imputation_data.shape
(10000, 14)
# Convert imputation_data array to DataFrame with column names
imputation_data = pd.DataFrame(imputation_data, columns = column_names)
imputation_data.index = data.index
imputation_data["Sales_volume"].head()
Date 1995-10-14 7.07 1995-10-15 8.35 1995-10-16 6.82 1995-10-17 6.86 1995-10-18 7.45 Name: Sales_volume, dtype: object
imputation_data.reset_index(inplace = True)
imputation_data.rename(columns = {"index": "Date"}, inplace = True)
imputation_data.head()
| Date | Sales | Sales_volume | Price/Kg | Diameter | Length | Current_stock | Re_order | Lead_time | Production_time | Units_Produced | Production_cost | Location | Climate | Grade | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1995-10-14 | 424440.0 | 7.07 | 60.0 | 32.0 | 13.0 | 10770.0 | 618.0 | 7.0 | 3.0 | 14828.0 | 18734.0 | Pune | Rainy | Fe-500 |
| 1 | 1995-10-15 | 426000.0 | 8.35 | 51.0 | 12.0 | 7.0 | 8039.0 | 1195.0 | 2.0 | 3.0 | 12795.0 | 21294.0 | Hyderabad | Rainy | Fe-415 |
| 2 | 1995-10-16 | 429360.0 | 6.82 | 63.0 | 12.0 | 9.0 | 7328.0 | 1372.0 | 14.0 | 6.0 | 13576.0 | 19759.0 | Kolkata | Rainy | Fe-500D |
| 3 | 1995-10-17 | 432000.0 | 6.86 | 63.0 | 8.0 | 25.0 | 3040.0 | 688.0 | 14.0 | 2.0 | 8060.0 | 23073.0 | Bengaluru | Rainy | Fe-550 |
| 4 | 1995-10-18 | 432000.0 | 7.45 | 58.0 | 32.0 | 27.0 | 9011.0 | 704.0 | 11.0 | 6.0 | 7532.0 | 19986.0 | Delhi | Rainy | Fe-500 |
imputation_data.to_csv("clean_data.csv")
# Create a new variable with "Date" and "Sales_volume" columns
sales_volume_data = imputation_data.loc[:, ["Date", "Sales_volume"]].copy()
sales_volume_data.head()
| Date | Sales_volume | |
|---|---|---|
| 0 | 1995-10-14 | 7.07 |
| 1 | 1995-10-15 | 8.35 |
| 2 | 1995-10-16 | 6.82 |
| 3 | 1995-10-17 | 6.86 |
| 4 | 1995-10-18 | 7.45 |
# Make a copy of the remaining columns
remaining_data = imputation_data.copy()
remaining_data.drop(columns=["Date", "Sales_volume"], inplace=True)
remaining_data.isna().sum()
Sales 0 Price/Kg 0 Diameter 0 Length 0 Current_stock 0 Re_order 0 Lead_time 0 Production_time 0 Units_Produced 0 Production_cost 0 Location 0 Climate 0 Grade 0 dtype: int64
sales_volume_data.isna().sum()
Date 0 Sales_volume 0 dtype: int64
# Convert "Date" column to datetime format
sales_volume_data["Date"] = pd.to_datetime(sales_volume_data["Date"])
# Set "Date" column as index
sales_volume_data.set_index("Date", inplace=True)
## Converting the Date Column in to Daily based
clean_data = sales_volume_data.loc['1995-10-14':'2023-02-28']
##clean_data = sales_volume_data['Sales_volume'].resample('D').sum()
clean_data
| Sales_volume | |
|---|---|
| Date | |
| 1995-10-14 | 7.07 |
| 1995-10-15 | 8.35 |
| 1995-10-16 | 6.82 |
| 1995-10-17 | 6.86 |
| 1995-10-18 | 7.45 |
| ... | ... |
| 2023-02-24 | 9.13 |
| 2023-02-25 | 9.99 |
| 2023-02-26 | 8.84 |
| 2023-02-27 | 7.64 |
| 2023-02-28 | 10.61 |
10000 rows × 1 columns
clean_data.info()
<class 'pandas.core.frame.DataFrame'> DatetimeIndex: 10000 entries, 1995-10-14 to 2023-02-28 Data columns (total 1 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Sales_volume 10000 non-null object dtypes: object(1) memory usage: 156.2+ KB
clean_data = pd.DataFrame(clean_data)
clean_data
| Sales_volume | |
|---|---|
| Date | |
| 1995-10-14 | 7.07 |
| 1995-10-15 | 8.35 |
| 1995-10-16 | 6.82 |
| 1995-10-17 | 6.86 |
| 1995-10-18 | 7.45 |
| ... | ... |
| 2023-02-24 | 9.13 |
| 2023-02-25 | 9.99 |
| 2023-02-26 | 8.84 |
| 2023-02-27 | 7.64 |
| 2023-02-28 | 10.61 |
10000 rows × 1 columns
import statsmodels.api as sm
# Perform seasonal decomposition for Additive
decomposition = sm.tsa.seasonal_decompose(clean_data, model ='additive', period = 365)
from pylab import rcParams
rcParams['figure.figsize'] = 12, 6
# Plot the decomposition
fig = decomposition.plot()
plt.show()
The p-values for both coefficients are very small (i.e., less than 0.05), indicating that both the intercept and slope terms are statistically significant. Therefore, you can conclude that the trend in your time series is linear, with a negative slope.
Performing Augumented-Dickey-Fuller Test to check whether data is stationary or not.
from statsmodels.tsa.stattools import adfuller
def adf_test(series):
result = adfuller(series)
print('ADF Statistics: {}'.format(result[0]))
print('p- value: {}'.format(result[1]))
if result[1] <= 0.05:
print("strong evidence against the null hypothesis, reject the null hypothesis. Data has no unit root and is stationary")
else:
print("weak evidence against the null hypothesis, fail to reject the null hypothesis. Data has a unit root and is non-stationary")
adf_test(clean_data['Sales_volume'])
ADF Statistics: -10.518718887039098 p- value: 9.809085180114798e-19 strong evidence against the null hypothesis, reject the null hypothesis. Data has no unit root and is stationary
The ADF statistic is a negative number, in this case -10.518718887039098, and it indicates the strength of the rejection of the null hypothesis. The null hypothesis is that the time series has a unit root, while the alternative hypothesis is that it does not have a unit root.
The p-value is a very small number, in this case 9.809085180114798e-19, which is smaller than the commonly used significance level of 0.05. This means that we have strong evidence against the null hypothesis and can reject it.
Therefore, the data has no unit root and is stationary. This means that the mean and variance of the time series data remain constant over time, making it easier to model and forecast.
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from pandas.plotting import autocorrelation_plot
plt.rcParams.update({'figure.figsize': (10,6), 'figure.dpi':120})
autocorrelation_plot(clean_data['Sales_volume'].astype(float).tolist())
<AxesSubplot: xlabel='Lag', ylabel='Autocorrelation'>
clean_data['Sales_volume'] = pd.to_numeric(clean_data['Sales_volume'])
clean_data['Sales_volume'].plot()
<AxesSubplot: xlabel='Date'>
ACF (Auto Correlation Function) and PACF (Partial Auto Correlation Function) plots are useful tools in time series analysis to identify the relationship between the observations in a time series.
ACF plots show the correlation between an observation in a time series and the previous observations. The ACF plot shows the correlation coefficient on the y-axis and the number of lags on the x-axis. A significant positive correlation at lag k in the ACF plot indicates that the observation at time t-k can be used to predict the observation at time t.
acf = plot_acf(clean_data)
PACF plots show the correlation between an observation and its lagged values after accounting for the effect of other lags. The PACF plot shows the correlation coefficient on the y-axis and the number of lags on the x-axis. A significant positive correlation at lag k in the PACF plot indicates that the observation at time t-k can be used to predict the observation at time t after removing the effects of all other lags.
pacf = plot_pacf(clean_data)
from sklearn.model_selection import train_test_split
# Split data into train and test sets
train_data, test_data = train_test_split(clean_data, test_size = 0.2, shuffle = False)
# Print the sizes of the train and test sets
print("Train set size:", len(train_data))
print("Test set size:", len(test_data))
Train set size: 8000 Test set size: 2000
from datetime import datetime, timedelta
train_dataset_end=datetime(2017,12,1)
test_dataset_end=datetime(2023,12,1)
train_data = clean_data[:train_dataset_end]
test_data = clean_data[train_dataset_end+timedelta(days=1):test_dataset_end]
train_data
| Sales_volume | |
|---|---|
| Date | |
| 1995-10-14 | 7.07 |
| 1995-10-15 | 8.35 |
| 1995-10-16 | 6.82 |
| 1995-10-17 | 6.86 |
| 1995-10-18 | 7.45 |
| ... | ... |
| 2017-11-27 | 7.75 |
| 2017-11-28 | 10.96 |
| 2017-11-29 | 8.85 |
| 2017-11-30 | 9.48 |
| 2017-12-01 | 7.75 |
8085 rows × 1 columns
test_data
| Sales_volume | |
|---|---|
| Date | |
| 2017-12-02 | 9.01 |
| 2017-12-03 | 9.11 |
| 2017-12-04 | 6.81 |
| 2017-12-05 | 7.84 |
| 2017-12-06 | 6.93 |
| ... | ... |
| 2023-02-24 | 9.13 |
| 2023-02-25 | 9.99 |
| 2023-02-26 | 8.84 |
| 2023-02-27 | 7.64 |
| 2023-02-28 | 10.61 |
1915 rows × 1 columns
ARIMA
ARIMA models are denoted as ARIMA(p,d,q), where p, d, and q are the orders of the AR, I, and MA components, respectively. For example, an ARIMA(2,1,1) model uses two past values of the time series, one difference, and one past error to forecast the future values.
from pmdarima.arima import auto_arima
# Convert train_data to float data type
train_data = train_data.astype('float')
test_data = test_data.astype('float')
# create and fit the model
model_ARIMA = ARIMA(train_data['Sales_volume'], order=(1, 1, 1), seasonal_order = None)
model_Arima_fit= model_ARIMA.fit()
model_Arima_fit.summary()
| Dep. Variable: | Sales_volume | No. Observations: | 8085 |
|---|---|---|---|
| Model: | ARIMA(1, 1, 1) | Log Likelihood | -14740.499 |
| Date: | Tue, 25 Apr 2023 | AIC | 29486.998 |
| Time: | 15:52:04 | BIC | 29507.991 |
| Sample: | 10-14-1995 | HQIC | 29494.180 |
| - 12-01-2017 | |||
| Covariance Type: | opg |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| ar.L1 | -0.0064 | 0.011 | -0.572 | 0.568 | -0.028 | 0.016 |
| ma.L1 | -0.9714 | 0.003 | -334.880 | 0.000 | -0.977 | -0.966 |
| sigma2 | 2.2447 | 0.035 | 64.752 | 0.000 | 2.177 | 2.313 |
| Ljung-Box (L1) (Q): | 0.00 | Jarque-Bera (JB): | 294.22 |
|---|---|---|---|
| Prob(Q): | 1.00 | Prob(JB): | 0.00 |
| Heteroskedasticity (H): | 1.13 | Skew: | 0.47 |
| Prob(H) (two-sided): | 0.00 | Kurtosis: | 3.08 |
model_Arima_fit.plot_diagnostics(figsize=(16, 8))
plt.show()
train_data = clean_data[:train_dataset_end]
test_data = clean_data[train_dataset_end+timedelta(days = 1):test_dataset_end]
# Prediction
pred_start_date = test_data.index[0]
pred_start_date
Timestamp('2017-12-02 00:00:00')
pred_end_date = test_data.index[-1]
pred_end_date
Timestamp('2023-02-28 00:00:00')
pred_ARIMA = model_Arima_fit.predict(start = pred_start_date, end = pred_end_date)
pred_ARIMA
2017-12-02 8.651630
2017-12-03 8.645836
2017-12-04 8.645873
2017-12-05 8.645873
2017-12-06 8.645873
...
2023-02-24 8.645873
2023-02-25 8.645873
2023-02-26 8.645873
2023-02-27 8.645873
2023-02-28 8.645873
Freq: D, Name: predicted_mean, Length: 1915, dtype: float64
residuals = test_data['Sales_volume'] - pred_ARIMA
residuals
Date
2017-12-02 0.358370
2017-12-03 0.464164
2017-12-04 -1.835873
2017-12-05 -0.805873
2017-12-06 -1.715873
...
2023-02-24 0.484127
2023-02-25 1.344127
2023-02-26 0.194127
2023-02-27 -1.005873
2023-02-28 1.964127
Length: 1915, dtype: float64
start_index = len(clean_data.Sales_volume)
start_index
end_index = start_index + 2000 ## predicting for approximately 5 years
forecast_ARIMA = model_Arima_fit.predict(start = start_index, end = end_index)
print(forecast_ARIMA)
# plot forecasts against actual outcomes
plt.plot(clean_data.Sales_volume)
plt.plot(forecast_ARIMA, color = 'red')
2023-03-01 8.645873
2023-03-02 8.645873
2023-03-03 8.645873
2023-03-04 8.645873
2023-03-05 8.645873
...
2028-08-17 8.645873
2028-08-18 8.645873
2028-08-19 8.645873
2028-08-20 8.645873
2028-08-21 8.645873
Freq: D, Name: predicted_mean, Length: 2001, dtype: float64
[<matplotlib.lines.Line2D at 0x1d673c2ead0>]
model_Arima_fit.resid.plot(kind='kde')
<AxesSubplot: ylabel='Density'>
test_data['Predicted_ARIMA'] = pred_ARIMA
test_data[['Sales_volume','Predicted_ARIMA']].plot()
<AxesSubplot: xlabel='Date'>
import matplotlib.pyplot as plt
# Plot train data in blue
plt.plot(train_data['Sales_volume'], color='blue', label='Train Data')
# Plot test data in green
plt.plot(test_data['Sales_volume'], color='green', label='Test Data')
# Plot forecasted values in yellow
plt.plot(pred_ARIMA, color='yellow', label='Predicted Data')
# Plot forecasted values in red
plt.plot(forecast_ARIMA, color='red', label='Forecasted Data')
# Set axis labels and title
plt.xlabel('Date')
plt.ylabel('Sales Volume')
plt.title('ARIMA Sales Volume Forecast')
# Add a legend to the plot
plt.legend()
# Display the plot
plt.show()
from sklearn.metrics import mean_squared_error
from math import sqrt
# Evaluate forecasts
rmse_arima = sqrt(mean_squared_error(test_data['Sales_volume'], pred_ARIMA))
print('Test RMSE: %.3f' % rmse_arima)
Test RMSE: 1.522
(Seasonal Autoregressive Integrated Moving Average) is an extension of the ARIMA model that takes into account seasonality in the data. SARIMA models are used to model time series data that exhibit seasonal patterns or periodic fluctuations.
The term "SARIMA" refers to the following components:
Seasonal Autoregression (SAR): SAR models are similar to AR models, but they use past values of the time series that are separated by a fixed seasonal interval. An SAR(P) model expresses the current value of the time series as a linear combination of the P most recent values that are separated by a seasonal interval.
Seasonal Moving Average (SMA): SMA models are similar to MA models, but they use past errors that are separated by a fixed seasonal interval. An SMA(Q) model expresses the current value of the time series as a linear combination of the Q most recent errors that are separated by a seasonal interval.
Seasonal Integration (SI): SI models are similar to I models, but they take seasonal differences between observations. An SI(D) model takes seasonal differences of order D.
Seasonal period (s): SARIMA models require specification of the length of the seasonal period, denoted by s. For example, if the data is monthly and exhibits annual seasonality, s=12.
## create a SARIMA model
from statsmodels.tsa.statespace.sarimax import SARIMAX
train_data = clean_data[:train_dataset_end]
test_data = clean_data[train_dataset_end+timedelta(days=1):test_dataset_end]
# Convert train_data to float data type
train_data = train_data.astype('float')
test_data = test_data.astype('float')
model_SARIMA = SARIMAX(train_data['Sales_volume'], order = (3,0,5), seasonal_order = (0,1,0,12))
model_SARIMA_fit = model_SARIMA.fit()
model_SARIMA_fit.summary()
| Dep. Variable: | Sales_volume | No. Observations: | 8085 |
|---|---|---|---|
| Model: | SARIMAX(3, 0, 5)x(0, 1, [], 12) | Log Likelihood | -17360.399 |
| Date: | Tue, 25 Apr 2023 | AIC | 34738.799 |
| Time: | 15:52:30 | BIC | 34801.765 |
| Sample: | 10-14-1995 | HQIC | 34760.341 |
| - 12-01-2017 | |||
| Covariance Type: | opg |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| ar.L1 | -0.0197 | 1.320 | -0.015 | 0.988 | -2.607 | 2.568 |
| ar.L2 | -0.0543 | 1.287 | -0.042 | 0.966 | -2.577 | 2.468 |
| ar.L3 | -0.0755 | 1.233 | -0.061 | 0.951 | -2.493 | 2.342 |
| ma.L1 | 0.0049 | 1.320 | 0.004 | 0.997 | -2.583 | 2.593 |
| ma.L2 | 0.0799 | 1.294 | 0.062 | 0.951 | -2.456 | 2.616 |
| ma.L3 | 0.0737 | 1.246 | 0.059 | 0.953 | -2.368 | 2.516 |
| ma.L4 | -0.0031 | 0.046 | -0.068 | 0.946 | -0.092 | 0.086 |
| ma.L5 | -0.0070 | 0.035 | -0.201 | 0.841 | -0.075 | 0.061 |
| sigma2 | 4.3189 | 0.065 | 66.311 | 0.000 | 4.191 | 4.447 |
| Ljung-Box (L1) (Q): | 0.00 | Jarque-Bera (JB): | 12.31 |
|---|---|---|---|
| Prob(Q): | 1.00 | Prob(JB): | 0.00 |
| Heteroskedasticity (H): | 1.16 | Skew: | 0.00 |
| Prob(H) (two-sided): | 0.00 | Kurtosis: | 3.19 |
# Define prediction start and end dates
pred_start_date = test_data.index[0]
pred_end_date = test_data.index[-1]
print("pred_start_date: ", pred_start_date)
print("pred_end_date: ", pred_end_date)
pred_start_date: 2017-12-02 00:00:00 pred_end_date: 2023-02-28 00:00:00
pred_SARIMA = model_SARIMA_fit.predict(start = pred_start_date, end = pred_end_date)
pred_SARIMA
2017-12-02 10.211170
2017-12-03 6.399324
2017-12-04 7.164405
2017-12-05 8.816614
2017-12-06 7.414579
...
2023-02-24 7.164403
2023-02-25 8.816614
2023-02-26 7.414580
2023-02-27 10.133867
2023-02-28 7.691441
Freq: D, Name: predicted_mean, Length: 1915, dtype: float64
residuals_SARIMA = test_data['Sales_volume'] - pred_SARIMA
residuals_SARIMA
Date
2017-12-02 -1.201170
2017-12-03 2.710676
2017-12-04 -0.354405
2017-12-05 -0.976614
2017-12-06 -0.484579
...
2023-02-24 1.965597
2023-02-25 1.173386
2023-02-26 1.425420
2023-02-27 -2.493867
2023-02-28 2.918559
Length: 1915, dtype: float64
model_SARIMA_fit.resid.plot()
<AxesSubplot: xlabel='Date'>
model_SARIMA_fit.resid.plot(kind='kde')
<AxesSubplot: ylabel='Density'>
# Define start and end indices for forecast
start_index = len(clean_data.Sales_volume)
end_index = start_index + 2000
# Forecast using SARIMA model
forecast_SARIMA = model_SARIMA_fit.predict(start=start_index, end=end_index)
print(forecast_SARIMA)
2023-03-01 7.749416
2023-03-02 10.959641
2023-03-03 8.849930
2023-03-04 9.480065
2023-03-05 7.750030
...
2028-08-17 7.750030
2028-08-18 10.211171
2028-08-19 6.399317
2028-08-20 7.164403
2028-08-21 8.816614
Freq: D, Name: predicted_mean, Length: 2001, dtype: float64
# plot forecasts against actual outcomes
plt.plot(clean_data.Sales_volume)
plt.plot(forecast_SARIMA, color = 'red')
[<matplotlib.lines.Line2D at 0x1d66e7ea0e0>]
# Add predicted values to test data
test_data['Predicted_SARIMA'] = pred_SARIMA
# Plot actual vs predicted values
test_data[['Sales_volume', 'Predicted_SARIMA']].plot()
<AxesSubplot: xlabel='Date'>
import matplotlib.pyplot as plt
# Plot train data in blue
plt.plot(train_data['Sales_volume'], color='blue', label='Train Data')
# Plot test data in green
plt.plot(test_data['Sales_volume'], color='green', label='Test Data')
# Plot forecasted values in yellow
plt.plot(pred_SARIMA, color='yellow', label='Predicted Data')
# Plot forecasted values in red
plt.plot(forecast_SARIMA, color='red', label='Forecasted Data')
# Set axis labels and title
plt.xlabel('Date')
plt.ylabel('Sales Volume')
plt.title('SARIMA Sales Volume Forecast')
# Add a legend to the plot
plt.legend()
# Display the plot
plt.show()
(Seasonal Autoregressive Integrated Moving Average with Exogenous Variables) is an extension of the SARIMA model that incorporates exogenous variables or external predictors into the model. Exogenous variables are independent variables that may be related to the time series being forecasted, but are not part of the time series itself.
The term "SARIMAX" refers to the following components:
Seasonal Autoregression (SAR): SAR models are similar to AR models, but they use past values of the time series that are separated by a fixed seasonal interval. An SAR(P) model expresses the current value of the time series as a linear combination of the P most recent values that are separated by a seasonal interval.
Seasonal Moving Average (SMA): SMA models are similar to MA models, but they use past errors that are separated by a fixed seasonal interval. An SMA(Q) model expresses the current value of the time series as a linear combination of the Q most recent errors that are separated by a seasonal interval.
Seasonal Integration (SI): SI models are similar to I models, but they take seasonal differences between observations. An SI(D) model takes seasonal differences of order D.
Exogenous variables (X): SARIMAX models incorporate exogenous variables into the model. These variables can be continuous or categorical and may be related to the time series being forecasted.
Seasonal period (s): SARIMAX models require specification of the length of the seasonal period, denoted by s.
# create and fit the SARIMAX model
model_SARIMAX = SARIMAX(train_data['Sales_volume'], order=(1, 1, 1), seasonal_order=(1, 1, 1, 12))
SARIMAX_fit = model_SARIMAX.fit()
SARIMAX_fit.summary()
| Dep. Variable: | Sales_volume | No. Observations: | 8085 |
|---|---|---|---|
| Model: | SARIMAX(1, 1, 1)x(1, 1, 1, 12) | Log Likelihood | -14761.927 |
| Date: | Tue, 25 Apr 2023 | AIC | 29533.853 |
| Time: | 15:53:39 | BIC | 29568.834 |
| Sample: | 10-14-1995 | HQIC | 29545.821 |
| - 12-01-2017 | |||
| Covariance Type: | opg |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| ar.L1 | -0.0063 | 0.011 | -0.558 | 0.577 | -0.028 | 0.016 |
| ma.L1 | -0.9719 | 0.003 | -333.080 | 0.000 | -0.978 | -0.966 |
| ar.S.L12 | 0.0143 | 0.011 | 1.300 | 0.194 | -0.007 | 0.036 |
| ma.S.L12 | -0.9997 | 0.015 | -66.695 | 0.000 | -1.029 | -0.970 |
| sigma2 | 2.2472 | 0.049 | 45.678 | 0.000 | 2.151 | 2.344 |
| Ljung-Box (L1) (Q): | 0.00 | Jarque-Bera (JB): | 249.04 |
|---|---|---|---|
| Prob(Q): | 0.98 | Prob(JB): | 0.00 |
| Heteroskedasticity (H): | 1.13 | Skew: | 0.43 |
| Prob(H) (two-sided): | 0.00 | Kurtosis: | 2.93 |
# Prediction
pred_start_date = test_data.index[0]
pred_end_date = test_data.index[-1]
pred_start_date
Timestamp('2017-12-02 00:00:00')
pred_end_date
Timestamp('2023-02-28 00:00:00')
pred_SARIMAX = SARIMAX_fit.predict(start = pred_start_date, end = pred_end_date)
pred_SARIMAX
2017-12-02 8.680630
2017-12-03 8.638383
2017-12-04 8.653938
2017-12-05 8.605424
2017-12-06 8.671492
...
2023-02-24 9.011166
2023-02-25 8.938174
2023-02-26 9.025930
2023-02-27 8.938362
2023-02-28 9.023409
Freq: D, Name: predicted_mean, Length: 1915, dtype: float64
residuals = test_data['Sales_volume'] - pred_SARIMAX
residuals
Date
2017-12-02 0.329370
2017-12-03 0.471617
2017-12-04 -1.843938
2017-12-05 -0.765424
2017-12-06 -1.741492
...
2023-02-24 0.118834
2023-02-25 1.051826
2023-02-26 -0.185930
2023-02-27 -1.298362
2023-02-28 1.586591
Length: 1915, dtype: float64
SARIMAX_fit.plot_diagnostics(figsize=(16, 8))
plt.show()
start_index = len(clean_data.Sales_volume)
start_index
end_index = start_index + 2000
forecast_SARIMAX = SARIMAX_fit.predict(start = start_index, end = end_index)
print(forecast_SARIMAX)
2023-03-01 8.986081
2023-03-02 8.988250
2023-03-03 8.939235
2023-03-04 8.986518
2023-03-05 9.042438
...
2028-08-17 9.393419
2028-08-18 9.343025
2028-08-19 9.359827
2028-08-20 9.364262
2028-08-21 9.291270
Freq: D, Name: predicted_mean, Length: 2001, dtype: float64
# plot forecasts against actual outcomes
plt.plot(clean_data.Sales_volume)
plt.plot(forecast_SARIMAX, color = 'red')
[<matplotlib.lines.Line2D at 0x1d653af3910>]
model_SARIMA_fit.resid.plot()
<AxesSubplot: xlabel='Date'>
model_SARIMA_fit.resid.plot(kind='kde')
<AxesSubplot: ylabel='Density'>
# Add predicted values to test data
test_data['Predicted_SARIMAX'] = pred_SARIMAX
# Plot actual vs predicted values
test_data[['Sales_volume', 'Predicted_SARIMAX']].plot()
<AxesSubplot: xlabel='Date'>
import matplotlib.pyplot as plt
# Plot train data in blue
plt.plot(train_data['Sales_volume'], color='blue', label='Train Data')
# Plot test data in green
plt.plot(test_data['Sales_volume'], color='green', label='Test Data')
# Plot forecasted values in yellow
plt.plot(pred_SARIMAX, color='yellow', label='Predicted Data')
# Plot forecasted values in red
plt.plot(forecast_SARIMAX, color='red', label='Forecasted Data')
# Set axis labels and title
plt.xlabel('Date')
plt.ylabel('Sales Volume')
plt.title('SARIMAX Sales Volume Forecast')
# Add a legend to the plot
plt.legend()
# Display the plot
plt.show()
Adaptive Exponential Smoothing (AES) is a time series forecasting method that uses an adaptive smoothing parameter to adjust the level of smoothing based on the recent trends and patterns in the time series. The AES model assumes that the future values of a time series are a function of the current and past values, and that the level of smoothing should be adjusted over time to capture changing trends and patterns.
from statsmodels.tsa.holtwinters import ExponentialSmoothing
# create and fit the model
model_AES = ExponentialSmoothing(train_data['Sales_volume'], trend = 'add', damped = True,
seasonal = 'add', seasonal_periods = 12)
model_fit = model_AES.fit()
residuals = model_fit.resid
residuals.plot(figsize=(16, 8))
plt.show()
# make predictions
pred_AES = model_fit.predict(start = pred_start_date, end = pred_end_date)
pred_AES
2017-12-02 8.649780
2017-12-03 8.666557
2017-12-04 8.671074
2017-12-05 8.598283
2017-12-06 8.686390
...
2023-02-24 8.671074
2023-02-25 8.598283
2023-02-26 8.686390
2023-02-27 8.597468
2023-02-28 8.682705
Freq: D, Length: 1915, dtype: float64
# calculate the residuals
residuals = test_data['Sales_volume'] - pred_AES
residuals
Date
2017-12-02 0.360220
2017-12-03 0.443443
2017-12-04 -1.861074
2017-12-05 -0.758283
2017-12-06 -1.756390
...
2023-02-24 0.458926
2023-02-25 1.391717
2023-02-26 0.153610
2023-02-27 -0.957468
2023-02-28 1.927295
Length: 1915, dtype: float64
# plot the predicted and actual values
test_data['Predicted_AES'] = pred_AES
test_data[['Sales_volume','Predicted_AES']].plot()
<AxesSubplot: xlabel='Date'>
start_index = len(clean_data.Sales_volume)
start_index
end_index = start_index + 2000
forecast_AES = model_fit.predict(start = start_index, end = end_index)
print(forecast_AES)
2023-03-01 8.645099
2023-03-02 8.646851
2023-03-03 8.597958
2023-03-04 8.644972
2023-03-05 8.700585
...
2028-08-17 8.700585
2028-08-18 8.649780
2028-08-19 8.666557
2028-08-20 8.671074
2028-08-21 8.598283
Freq: D, Length: 2001, dtype: float64
# plot forecasts against actual outcomes
plt.plot(clean_data.Sales_volume)
plt.plot(forecast_AES, color = 'red')
[<matplotlib.lines.Line2D at 0x1d67136aa40>]
# Add predicted values to test data
test_data['Predicted_AES'] = pred_AES
# Plot actual vs predicted values
test_data[['Sales_volume', 'Predicted_AES']].plot()
<AxesSubplot: xlabel='Date'>
import matplotlib.pyplot as plt
# Plot train data in blue
plt.plot(train_data['Sales_volume'], color='blue', label='Train Data')
# Plot test data in green
plt.plot(test_data['Sales_volume'], color='green', label='Test Data')
# Plot forecasted values in yellow
plt.plot(pred_AES, color='yellow', label='Predicted Data')
# Plot forecasted values in red
plt.plot(forecast_AES, color='red', label='Forecasted Data')
# Set axis labels and title
plt.xlabel('Date')
plt.ylabel('Sales Volume')
plt.title('AES Sales Volume Forecast')
# Add a legend to the plot
plt.legend()
# Display the plot
plt.show()
Moving Average (MA) is a time series forecasting method that uses the past errors (or residuals) of a time series model to generate future predictions. The MA model assumes that the future values of a time series are a function of the past errors or residuals, and that the errors follow a white noise process with constant variance.
from statsmodels.tsa.arima.model import ARIMA
# create a rolling mean series with window size 5
rolling_mean = clean_data['Sales_volume'].rolling(window = 5).mean()
# create a new DataFrame with the rolling mean series
rolling_mean_data = pd.DataFrame({'Sales_volume': rolling_mean}, index = data.index)
# drop any rows with NaN values
rolling_mean_data.dropna(inplace = True)
# fit the ARIMA model
model = ARIMA(rolling_mean_data, order = (0, 0, 1))
model_fit = model.fit()
# print model summary
print(model_fit.summary())
SARIMAX Results
==============================================================================
Dep. Variable: Sales_volume No. Observations: 9996
Model: ARIMA(0, 0, 1) Log Likelihood -8068.726
Date: Tue, 25 Apr 2023 AIC 16143.452
Time: 15:54:03 BIC 16165.082
Sample: 10-18-1995 HQIC 16150.774
- 02-28-2023
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
const 8.5458 0.009 941.243 0.000 8.528 8.564
ma.L1 0.6445 0.007 87.533 0.000 0.630 0.659
sigma2 0.2942 0.004 74.360 0.000 0.286 0.302
===================================================================================
Ljung-Box (L1) (Q): 1869.78 Jarque-Bera (JB): 181.97
Prob(Q): 0.00 Prob(JB): 0.00
Heteroskedasticity (H): 0.65 Skew: 0.28
Prob(H) (two-sided): 0.00 Kurtosis: 3.34
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
# plot the original data and the rolling mean
plt.plot(clean_data['Sales_volume'], label = 'Original Data')
plt.plot(rolling_mean_data['Sales_volume'], label = 'Rolling Mean')
plt.legend()
plt.show()
# plot the model diagnostics
model_fit.plot_diagnostics(figsize=(16, 8))
plt.show()
# make predictions
pred_MA = model_fit.predict(start = pred_start_date, end=pred_end_date, typ ='levels')
pred_MA
2017-12-02 8.732061
2017-12-03 8.853798
2017-12-04 8.536882
2017-12-05 8.478180
2017-12-06 8.304622
...
2023-02-24 8.613490
2023-02-25 8.744604
2023-02-26 8.874071
2023-02-27 8.998155
2023-02-28 8.544386
Freq: D, Name: predicted_mean, Length: 1915, dtype: float64
# calculate the residuals
residuals_MA = test_data['Sales_volume'] - pred_MA
residuals_MA
Date
2017-12-02 0.277939
2017-12-03 0.256202
2017-12-04 -1.726882
2017-12-05 -0.638180
2017-12-06 -1.374622
...
2023-02-24 0.516510
2023-02-25 1.245396
2023-02-26 -0.034071
2023-02-27 -1.358155
2023-02-28 2.065614
Length: 1915, dtype: float64
start_index = len(clean_data.Sales_volume)
start_index
end_index = start_index + 2000
forecast_MA = model_fit.predict(start = start_index, end = end_index)
print(forecast_MA.head())
2023-03-05 8.545775 2023-03-06 8.545775 2023-03-07 8.545775 2023-03-08 8.545775 2023-03-09 8.545775 Freq: D, Name: predicted_mean, dtype: float64
# plot forecasts against actual outcomes
plt.plot(clean_data.Sales_volume)
plt.plot(forecast_MA, color = 'red')
[<matplotlib.lines.Line2D at 0x1d673c2c070>]
# plot the predicted and actual values
test_data['Predicted_MA'] = pred_MA
test_data[['Sales_volume','Predicted_MA']].plot()
<AxesSubplot: xlabel='Date'>
import matplotlib.pyplot as plt
# Plot train data in blue
plt.plot(train_data['Sales_volume'], color='blue', label='Train Data')
# Plot test data in green
plt.plot(test_data['Sales_volume'], color='green', label='Test Data')
# Plot forecasted values in yellow
plt.plot(pred_MA, color='yellow', label='Predicted Data')
# Plot forecasted values in red
plt.plot(forecast_MA, color='red', label='Forecasted Data')
# Set axis labels and title
plt.xlabel('Date')
plt.ylabel('Sales Volume')
plt.title('MA Sales Volume Forecast')
# Add a legend to the plot
plt.legend()
# Display the plot
plt.show()
Simple Exponential Smoothing (SES) is a time series forecasting method that uses a single smoothing parameter to generate a smoothed series and make future predictions. The SES model assumes that the next value in the time series is a function of the current value and an error term.
from statsmodels.tsa.holtwinters import SimpleExpSmoothing
model_SimpleExp = SimpleExpSmoothing(train_data['Sales_volume'])
SimpleExp_fit = model_SimpleExp.fit()
SimpleExp_fit.summary()
| Dep. Variable: | Sales_volume | No. Observations: | 8085 |
|---|---|---|---|
| Model: | SimpleExpSmoothing | SSE | 18147.333 |
| Optimized: | True | AIC | 6540.828 |
| Trend: | None | BIC | 6554.824 |
| Seasonal: | None | AICC | 6540.833 |
| Seasonal Periods: | None | Date: | Tue, 25 Apr 2023 |
| Box-Cox: | False | Time: | 15:54:09 |
| Box-Cox Coeff.: | None |
| coeff | code | optimized | |
|---|---|---|---|
| smoothing_level | 0.0280590 | alpha | True |
| initial_level | 7.2236349 | l.0 | True |
# create and fit the model
pred_SimpleExp = SimpleExp_fit.predict(start = pred_start_date, end = pred_end_date)
pred_SimpleExp
2017-12-02 8.646773
2017-12-03 8.646773
2017-12-04 8.646773
2017-12-05 8.646773
2017-12-06 8.646773
...
2023-02-24 8.646773
2023-02-25 8.646773
2023-02-26 8.646773
2023-02-27 8.646773
2023-02-28 8.646773
Freq: D, Length: 1915, dtype: float64
residuals_SimpleExp = test_data['Sales_volume'] - pred_SimpleExp
residuals_SimpleExp
Date
2017-12-02 0.363227
2017-12-03 0.463227
2017-12-04 -1.836773
2017-12-05 -0.806773
2017-12-06 -1.716773
...
2023-02-24 0.483227
2023-02-25 1.343227
2023-02-26 0.193227
2023-02-27 -1.006773
2023-02-28 1.963227
Length: 1915, dtype: float64
start_index = len(clean_data.Sales_volume)
start_index
end_index = start_index + 2000
forecast_SimpleExp = model_fit.predict(start = start_index, end = end_index)
print(forecast_SimpleExp)
2023-03-05 8.545775
2023-03-06 8.545775
2023-03-07 8.545775
2023-03-08 8.545775
2023-03-09 8.545775
...
2028-08-21 8.545775
2028-08-22 8.545775
2028-08-23 8.545775
2028-08-24 8.545775
2028-08-25 8.545775
Freq: D, Name: predicted_mean, Length: 2001, dtype: float64
# plot forecasts against actual outcomes
plt.plot(clean_data.Sales_volume)
plt.plot(forecast_SimpleExp, color = 'red')
[<matplotlib.lines.Line2D at 0x1d688e13f10>]
# Add predicted values to test data
test_data['Predicted_SimpleExp'] = pred_SimpleExp
# Plot actual vs predicted values
test_data[['Sales_volume', 'Predicted_SimpleExp']].plot()
<AxesSubplot: xlabel='Date'>
import matplotlib.pyplot as plt
# Plot train data in blue
plt.plot(train_data['Sales_volume'], color='blue', label='Train Data')
# Plot test data in green
plt.plot(test_data['Sales_volume'], color='green', label='Test Data')
# Plot forecasted values in yellow
plt.plot(pred_SimpleExp, color='yellow', label='Predicted Data')
# Plot forecasted values in red
plt.plot(forecast_SimpleExp, color='red', label='Forecasted Data')
# Set axis labels and title
plt.xlabel('Date')
plt.ylabel('Sales Volume')
plt.title('SimpleExp Sales Volume Forecast')
# Add a legend to the plot
plt.legend()
# Display the plot
plt.show()
import math
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error
rmse = sqrt(mean_squared_error(test_data.Sales_volume, pred_ARIMA))
print('ARIMA_RMSE: %.3f' % rmse)
mape = sqrt(mean_absolute_error(test_data.Sales_volume, pred_ARIMA)*100)
print('ARIMA_MAPE: %.3f' % mape)
ARIMA_RMSE: 1.522 ARIMA_MAPE: 11.179
rmse = sqrt(mean_squared_error(test_data.Sales_volume, pred_SARIMA))
print('SARIMA RMSE: %.3f' % rmse)
mape = sqrt( mean_absolute_error(test_data.Sales_volume,pred_SARIMA)*100)
print('SARIMA MAPE: %.3f' % mape)
SARIMA RMSE: 2.016 SARIMA MAPE: 12.701
rmse = sqrt(mean_squared_error(test_data.Sales_volume, pred_SARIMAX))
print('SARIMAX_RMSE: %.3f' % rmse)
mape = sqrt( mean_absolute_error(test_data.Sales_volume,pred_SARIMAX)*100)
print('SARIMAX_MAPE: %.3f' % mape)
SARIMAX_RMSE: 1.550 SARIMAX_MAPE: 11.312
rmse = sqrt(mean_squared_error(test_data.Sales_volume, pred_SimpleExp))
print('SimpleExp_RMSE: %.3f' % rmse)
mape = sqrt( mean_absolute_error(test_data.Sales_volume,pred_SimpleExp)*100)
print('SimpleExp_MAPE: %.3f' % mape)
SimpleExp_RMSE: 1.522 SimpleExp_MAPE: 11.179
rmse = sqrt(mean_squared_error(test_data.Sales_volume, pred_AES))
print('AES_RMSE: %.3f' % rmse)
mape = sqrt( mean_absolute_error(test_data.Sales_volume,pred_AES)*100)
print('AES_MAPE: %.3f' % mape)
AES_RMSE: 1.523 AES_MAPE: 11.181
rmse = sqrt(mean_squared_error(test_data.Sales_volume, pred_MA))
print('MA_RMSE: %.3f' % rmse)
mape = sqrt( mean_absolute_error(test_data.Sales_volume,pred_MA)*100)
print('MA_MAPE: %.3f' % mape)
MA_RMSE: 1.552 MA_MAPE: 11.237
from tabulate import tabulate
table = [["Model", "RMSE", "MAPE"],
["ARIMA", "1.522", "11.179"],
["SARIMA", "2.016", "12.701"],
["SARIMAX", "1.550", "11.312"],
["SimpleExp", "1.522", "11.179"],
["AES", "1.523", "11.181"],
["MA", "1.552", "11.237"]]
print(tabulate(table, headers="firstrow", tablefmt="fancy_grid"))
╒═══════════╤════════╤════════╕ │ Model │ RMSE │ MAPE │ ╞═══════════╪════════╪════════╡ │ ARIMA │ 1.522 │ 11.179 │ ├───────────┼────────┼────────┤ │ SARIMA │ 2.016 │ 12.701 │ ├───────────┼────────┼────────┤ │ SARIMAX │ 1.55 │ 11.312 │ ├───────────┼────────┼────────┤ │ SimpleExp │ 1.522 │ 11.179 │ ├───────────┼────────┼────────┤ │ AES │ 1.523 │ 11.181 │ ├───────────┼────────┼────────┤ │ MA │ 1.552 │ 11.237 │ ╘═══════════╧════════╧════════╛
import pickle
# Save the model
pickle.dump(model_Arima_fit, open('model_arima.pkl', 'wb'))
import os
os.getcwd()
'C:\\Users\\hp\\Desktop\\001 project code 2\\Code IPNB'
# Load the model
model = pickle.load(open('model_arima.pkl', 'rb'))
model
<statsmodels.tsa.arima.model.ARIMAResultsWrapper at 0x1d603636950>
clean_data.to_csv('TMT_Steel_Data.csv')
os.getcwd()
'C:\\Users\\hp\\Desktop\\001 project code 2\\Code IPNB'